The Odyssey of:
Shape Matching with Ordered
Boundary Point Shape Contexts
Using a Least Cost Diagonal Method
By Dr. Carl E Abrams
March 14th, 2015
1
•Odyssey:
a long wandering and eventful journey
Or
If we knew what we were doing, it wouldn't
be called research, would it? – A Einstein
2
Agenda
• Topic Selection
• The Eventful Journey
– Elation
– Mild Desperation
– Elation
– Desperation (and beyond)
– Relief (success)
• Discussion / Lessons Learned
3
Topic Selection or…
• First Idea: Requirements Engineering and
Business Process Modeling
• I despised the topic area
4
Topic Selection or…Roots
• Identification of Automotive Vehicles Using
Semantic Feature Extraction (Dec 2004)
Elation!
5
Dissertation Timeline
Carl E. Abrams
Draft Chap 4&5
Now!
Draft Chap 3&4
Defense
1/06
1/05
1/04
9/03
Draft Idea Paper
Complete Draft
Proposal
Advisor Selection
Complete
Dissertation
First Paper at
Proposal
Pace Day
Final Manuscript
and Paper
Committee
Formation
Final Draft Chap
1-3
Draft Chap 1&2
6
The Eventful Journey
7
Semantic Geometric Features: A
Preliminary Investigation of
Automobile Identification
Carl E. Abrams
Sung-Hyuk Cha, Michael Gargano, and Charles
Tappert
8
Agenda
• Overview of the Problem
• The Experiments
• Results
• Going Forward
9
Overview
• Object recognition remains a hard problem
• The human mind uses shapes to
recognize objects
• Can semantic features defined by their
shapes be more effective in the
recognition and identification of objects?
10
The Experiments
• 10 test images of cars
• Directly form the manufactures websites
• Images were restricted to side views of the cars taken
from 90 degrees
• All 2005 models
• Feature vectors calculated/measured from the images
11
The Vehicles
Honda Accord Sedan 2005
Honda Civic Coupe 2005
Mazda 3 2005
Mazda 6 2005
Porsche Carrera
Toyota Camry 2005
Toyota Corolla CE 2005
Toyota Celica GT 2005
Toyota Echo 2005
VW Passat 2005
12
Experiments used Euclidean
Distance
as the Measure
d
L
 (x  t )
i 1
i
2
i
the xi and ti are measurements from two different vehicles
13
Experiments used Euclidean
Distance
as the Measure
(x2,y2)
c
b
(x1,y1)
a
c = (a2+b2)1/2
c = ((x1-x2)2+(y1-y2)2)1/2
14
Manufacturers Specifications
First Experiment
Vehicle Description
Wheelbase in inchesWidth
Honda Accord Civic Coupe 2005
103.1
66.7
Honda Accord Sedan 2005
107.9
71.5
Mazda 3 2005
103.9
69.9
Mazda 6 2005
105.3
70.1
Porsche Carerra 2005
92.52
71.18
Toyota Camry 2005
107.1
70.7
Toyota Celica GT 2005
102.4
68.3
Toyota Corolla CE 2005
102.4
66.9
Toyota Echo 2005
92.3
65.4
VW Passat 2005
106.4
68.7
Length
175.4
189.5
178.3
186.8
174.29
189.2
170.5
178.3
164.8
185.2
Height
55.1
57.1
57.7
56.7
51.57
58.7
51.4
58.5
59.4
57.6
15
Boundary Description using Rays
Second Experiment
Vehicle Description
Honda Accord Civic Coupe 2005
Honda Accord Sedan 2005
Mazda 3 2005
Mazda 6 2005
Mercedes Benz C230 Sports Coupe 2005
Nissan Altima 2005
Porsche Carerra 2005
Toyota Camry 2005
Toyota Celica GT 2005
Toyota Corolla CE 2005
Toyota Echo 2005
VW Passat 2005
Wheelbase in mm
148
139
146
140
138
136
128
136
134
144
154
145
Ray length in mm at angles in degress
5
10
15
20
125
121
123
110
120
116
118
110
118
113
115
113
118
104
115
109
102
115
112
115
115
110
110
110
112
112
115
100
126
127
126
120
102
104
111
113
119
117
120
113
125
124
127
112
122
120
120
110
25
95
98
100
95
100
91
90
101
87
96
113
96
30
90
90
94
88
94
86
82
94
82
93
108
90
35
86
85
89
84
89
81
76
88
75
89
100
86
40
81
80
84
80
82
76
71
86
68
84
99
82
45
78
78
80
76
79
71
67
73
65
82
96
74
50
75
73
78
72
73
66
66
73
63
77
92
72
55
70
70
71
69
71
63
62
71
60
73
87
69
60
66
67
67
63
67
62
60
68
57
69
84
66
16
Semantic Features
Third Experiment
Vehicle Description
Honda Accord Civic
Coupe 2005
Honda Accord
Sedan 2005
Mazda 3 2005
Mazda 6 2005
Mercedes Benz
C230 Sports Coupe
2005
Wheelbase in mm
Angle FD Length FD Angle RD Length RD Angle Mirror Length Mirror
148
123
33
0
0
51
50
139
146
140
98
92
93
28
30
29
154
150
151
63
58
64
51
51
47
47
50
48
138
126
37
0
0
52
43
Nissan Altima 2005
Porsche Carerra
2005
136
98
25
153
58
47
45
128
144
33
0
0
69
34
Toyota Camry 2005
Toyota Celica GT
2005
Toyota Corolla CE
2005
Toyota Echo 2005
VW Passat 2005
136
85
24
151
62
42
53
134
125
31
0
0
49
44
144
154
145
90
97
97
34
34
36
148
0
154
63
0
59
42
49
47
58
65
50
17
Challenge: Determine the
qualitative ability of the feature
vectors to separate the vehicles
• Within each experiment compute the
distance of each vehicle from all the
others
• Evenly divide the measures into 5 bins
• Observe the distribution of the measures
18
The Results
50
45
40
35
30
25
20
15
10
5
0
HWD
Semantic
Rays
Bin1 Bin2 Bin3 Bin4 Bin5
19
Distance Matrix – Semantic
Features
Honda
Civic
Honda
Accord
Mazda 3
Mazda 6
Porsche
Carerra
Toyota
Camry
Toyota
Celica
Toyota
Corolla
Toyota
Echo
VW
Passat
Honda
Civic
0
156.02
153.17
154.00
27.66
155.96
2.82
151.90
26.07
156.23
Honda
Accord
156.02
0
7.21
7.07
161.72
16.09
156.36
13.45
154.01
4.12
Mazda 3
153.17
7.21
0
4.24
159.78
11.44
153.60
9.43
150.09
7.55
Mazda 6
154.00
7.07
4.24
0
160.89
9.43
154.37
6.55
151.06
5.00
Porsche
Carerra
27.65
161.73
159.77
160.89
0
164/35
27.58
159.84
51.08
162.50
Toyota
Camry
155.96
16.09
11.44
9.43
164.35
0
156.36
5.83
151.63
13.34
Toyota
Celica
2.82
156.36
153.60
154.36
27.58
156.36
0
152.24
28.00
156.53
Toyota
Corolla
151.90
13,45
9.43
6.55
159.84
5.83
152.24
0
148.33
10.48
Toyota
Echo
26.07
154.01
150.09
151.06
51.07
151.63
28.00
148.33
0
154.01
VW Passat
156.23
4.12
7.55
5.00
162.50
13.34
156.53
10.48
154.01
0
20
Question #1 for the Class
• What is the difference between the human
hand and an automobile?
• Answer: Nothing!
• Commit the following to memory: A novel
application of existing methods does not a
dissertation topic make
21
End of First Dead End
Mild Desperation
22
Dissertation Timeline
Carl E. Abrams
Mild Desperation
sets in
Draft Chap 4&5
Now!
Draft Chap 3&4
Defense
1/06
1/05
1/04
9/03
Draft Idea Paper
Complete Draft
Proposal
Advisor Selection
Complete
Dissertation
First Paper at
Proposal
Pace Day
Final Manuscript
and Paper
Committee
Formation
Final Draft Chap
1-3
Draft Chap 1&2
23
Shape Contexts
• Shape Contexts are a novel shape
descriptor introduced be Belongie[1]
• Describes a shape by quantifying each
point on the boundary of a shape by its
relationship to all the on the boundary
points on the shape
• Compares shapes by comparing shape
contexts
[1] S. Belongie, "Image segmentation and shape matching for object recognition," vol. PhD, 2000, pp. 60.
24
Shape Contexts-Constructing
x
3
xx
4
5
xx

r
6 xx
x 2
1
1
xx
log r
0
30
60
90
120
150
180
210
Angle in Degrees
240
270
300
330
360
25
Shape Contexts-Constructing
30deg
2
1.732
60deg
90deg
1
Point1
Point
1
2
3
x
0
1
1
y
0
1.732
0
angle
0
60
0
turning
angle
270
180
60
adj angle
0
240
300
Point 2
distance
0.0000
2.0000
1.0000
normalized
distance
0.0000
1.2680
0.6340
angle
240
0
270
turning
angle
270
180
60
adj angle
330
0
210
Point 3
distance
2.0000
0.0000
1.7320
normalized
distance
1.2680
0.0000
1.0981
angle
180
90
0
turning
angle
270
180
60
adj angle
270
270
0
distance
1.0000
1.7320
0.0000
26
normalized
distance
0.6340
1.0981
0.0000
Shape Contexts-Comparing
90
80
70
60
50
40
30
20
10
0
Half Size Triangle
Full Size Triangle
90
80
70
60
50
40
30
20
10
0
Count
1
Bin 1
1
Bin 3
2
Bin 5
3
…Bin
60
90
80
70
60
50
40
30
20
10
0
0 Known 2
4
CHI^2 Test where K= # of bins, g is unknown histogram and h is the
2
0
4
known histogram 2
Count
Bin 1
Bin 3
Bin 5
Unknown
…Bin
60
Count
Bin 1
3
4
4
90
80
70
60
50
40
30
20
10
0
0
Bin 3
Bin 5
…Bin
60
Known
Count
Bin 1
Bin 3
Bin 5
…Bin
60
Known
27
Shape Contexts-Properties: Translation
x
x
x
x
x
x
Points
X
1
0
Y
0
2
1
1.7321
3
2
3.4642
4
2
1.7321
5
2
0
6
1
0
x
x
x
x
x
x
Points
X
1
1
Y
1
2
2
2.7321
3
3
4.4642
4
3
2.7321
5
3
1
6
2
1
28
Shape Contexts-Properties: Scale
x
x
x
x
x
x
x
x
x
x
x
x
Points
X
Y
Points
X
1
0
0
1
0
Y
0
2
1
1.7321
2
.5
0.866
3
2
3.4642
3
1
1.7321
4
2
1.7321
4
1
0.866
5
2
0
5
1
0
6
1
0
6
.5
0
Point
1
2
3
4
5
6
Point
1
0
6
10
10
10
4
2
6
0
6
6
10
6
3
10
6
0
6
10
10
4
10
6
6
0
6
6
5
10
10
10
6
0
6
6
4
6
10
6
6
0
29
Shape Contexts-Properties: Rotation
x
x
x
x
x
x
Points
X
1
0
Y
0
2
1
1.7321
3
2
3.4642
4
2
1.7321
5
2
0
6
1
0
x
4
x
x
2
x
x
x
3.4642
X
1
0
Y
0
2
0
1.0
3
0
2.0
4
1.7321
1.0
5
3.4642
0
6
1.7321
0
Point
Points
1
2
3
4
5
6
1
6.00
8.00
8.00
6.00
0.00
8.00
2
4.00
6.00
6.00
10.00
8.00
0.00
Point
3
4
0.00
8.00
8.00
0.00
3.33
6.00
10.00 10.00
6.00
8.00
4.00
6.00
5
3.33
6.00
0.00
10.00
8.00
6.00
6
10.00
10.00
10.00
0.00
6.00
10.00
30
Progress Report
The Role of Semantic Features
in Automobile Identification
as of December 10th , 2005
Carl E. Abrams
31
Agenda
• Review of Topic and Approach (Elevator Pitch)
• Summary Results to Date
• Next Steps
32
Review of Topic and Approach
• Develop an image segmentation and feature
extraction / classification scheme for automobiles
that employs the shapes of “semantic” parts and
their geometric relationships.
– Semantic features are the shapes of : windows, doors, front
and rear quarter panels.
33
Review of Topic and Approach
• Approach:
Develop a test database of vehicles by collecting side images
Develop/beg borrow or steal/software to interactively extract the shape
and geometric information from the images
Work through all the classification test database
In parallel continue building the master database
– Develop, test and compare segmentation / extraction method for
semantic shapes
34
Preliminary Results-Test DB
As of Oct 15: All Ford models back to 1990 ~ 50 images
As of Nov 12: Acura, Audi, Chrylser, Dodge, Ford, Honda,
Mercury, Nissan, Pontiac, Saab, Saturn, Toyota, Volvo, VW
models back to 1990 ~125 images
35
Preliminary Results-Image Segmentation Software
• Modified CTMRedit: a matlab GUI for viewing,
segmenting, and interpolating CT and MRI Images
– Written by Mark Hasegawa-Johnson and Jul Cha
• Simplified GUI and added capability to store shapes
specific to vehicle identification
36
Preliminary Results-Image Segmentation Software Examples
37
Preliminary Results
• Create a feature vector that allows the comparison of
one shape to another:
Vector_Known(ws1,ws2,ws3,ws4,ds1,ds2,body shape)
Vector_Unknown(ws1,ws2,ws3,ws4,ds1,ds2,body shape)
Feature vector depends on shape descriptor in this case “Shape
Contexts”
38
Preliminary Results – Shape Contexts
• How to effectively describe a shape?
r
o
39
Preliminary Results – Shape Contexts
• How to effectively describe a shape?
r
o
r (5 bins)
O (12 Bins)
40
Preliminary Results – Shape Contexts
• How to effectively describe a shape?
90
80
70
60
50
40
30
20
10
0
Count
Bin 1
Bin 3
Bin 5
…Bin
60
Develop the Shape Context histograms for every point on
the shape
41
Preliminary Results – Distance Between Shape Contexts
• How to effectively describe a shape?
90
80
70
60
50
40
30
20
10
0
Count
Bin 1
Bin 3
Bin 5
…Bin
60
90
80
70
60
50
40
30
20
10
0
Known
90
80
70
60
50
40
30
20
10
0
CHI^2 Test where K= # of bins, g is unknown histogram and h is the
known histogram
Count
Count
Bin 1
Bin 1
Bin 3
Bin 5
…Bin
60
Bin 3
Bin 5
…Bin
60
Known
90
80
70
60
50
40
30
20
10
0
Each shape has 128 points, creates a 128x128 cost matrix
Unknown
Count
Bin 1
Bin 3
Bin 5
…Bin
60
Known
42
Preliminary Results – Distance Between Shape Contexts
•
What is the best fit (minimum cost) to align all the points?
•
The Assignment Problem – Hungarian Method for bi-partite matching problem
We will be working with the following problem: assign n = 9 candidates to n=9 jobs to minimize the total salary cost
paid by the department. The individual salaries of each candidate at each job position depend on their qualification
and are given by the cost matrix (in $ per hour):
Sa
m
Jill
John
Liz
Ann
Lois
Pete
Alex
Herb
Administrator
20
15
10
10
17
23
25
5
15
Secretary to the Chair
10
10
12
15
9
7
8
7
8
Undergraduate Secretary
12
9
9
10
10
5
7
13
9
Graduate Secretary
13
14
10
15
15
5
8
20
10
Financial Clerk
12
13
10
15
14
5
9
20
10
Secretary
15
14
15
16
15
5
10
20
10
Web designer
7
9
12
12
7
6
7
15
12
Receptionist
5
6
8
8
5
4
5
10
7
Typist
5
6
8
8
5
4
5
10
7
If we start with the position of Administrator and assign it to Alex (he gets the minimal salary for this position), then we assign
the position of Secretary to Chair to Lois (he gets the minimal salary for this position), and so on, up to the position of Typist,
then the assignment is given by the assignment matrix:
43
Preliminary Results
• Experimental Setup – Run 5 test cars against known database of 50
cars
• Test cars re-segmented from known database
• Plot out matches based on Euclidean Distance
• Repeat by adding more cars of a different manufacturer to known
DB
44
Preliminary Results
300
Unknown: 2003FordMustang2DGT
250
200
Series1
150
100
50
0
2003FordFocus4DSE
2000FordCrownVictoria4D
2004FordCrownVictoriaSedan4D
2003FordCrownVictoria4D
2003FordFocus4DZTS
2001FordCrownVictoria4D
2000FordContour4D
2003FordTaurus4dSES
2002FordCrownVictoria4D
2004FordCrownVictoria4DLX
2002FordFocus4DZTS
2000FordFocus4DLX
2001FordFocus4DLX
2005FordCrownVictoria4D
2001FordFocus4DSE
2000FordTaurus4DSE
2005FordTaurusSE4D
2001FordTaurus4DSES
2005FordFiveHundred4DSE
2001FordEscort4DSedan
2004FordFocus4DZTW
2005FordFocusZX54D
2002FordTaurus4DSES
2002FordEscort4DSedan
2002FordFocus4Dwag
2004FordFocus4DZX5
2002FordFocus4DSE
2004FordTaurus4DSE
2004FordFocus4DZTS
2004FordFocus4DSVT
2003FordFocus4DZX5
2000FordFocus4Dwag
2003FordFoucs4Dwag
2001FordFocus4dwag
2004FordFocus4Dwag
2002FordFocus4DZX5
2001FordMustang2DGT
2000FordEscort4DSedan
2003FordFocusZX3
2005FordFocusZX32D
2002FordFocus2DZX3
2004FordFoucs2DZX3
2003FordFocus2DZX3
2000FordFocus2DZX3
2001FordFocus2DZX3
2005FordMustangCoupe2DV6Deluxe
2003FordMustang2DGT
2000FordMustang2DGT
2004FordMustang2D
2002FordMustang2DGT
45
Preliminary Results
Unknown: 2003FordMustang2DGT-Volvos Added to Known DB
300
250
200
Series1
150
100
50
0
2003FordFocus4DSE
2000FordCrownVictoria4D
2004FordCrownVictoriaSedan4D
2000VolvoS704D
2003VolvoS804D
2003FordCrownVictoria4D
2003FordFocus4DZTS
2001FordCrownVictoria4D
2000FordContour4D
2003FordTaurus4dSES
2002FordCrownVictoria4D
2004FordCrownVictoria4DLX
2002FordFocus4DZTS
2000FordFocus4DLX
2005VolvoS804D
2001FordFocus4DLX
2005FordCrownVictoria4D
2003VolvoS404D
2002VolvoS804D
2001VolvoS404D
2001FordFocus4DSE
2000VolvoS804D
2001VolvoS804D
2004VolvoS804D
2000FordTaurus4DSE
2005FordTaurusSE4D
2001FordTaurus4DSES
2000VolvoS404D
2005FordFiveHundred4DSE
2001FordEscort4DSedan
2004FordFocus4DZTW
2005FordFocusZX54D
2002FordTaurus4DSES
2002FordEscort4DSedan
2002FordFocus4Dwag
2004VolvoS604D
2004FordFocus4DZX5
2002FordFocus4DSE
2004FordTaurus4DSE
2004FordFocus4DZTS
2005VolvoS404D
2004FordFocus4DSVT
2003FordFocus4DZX5
2001VolvoS604D
2002VolvoS604D
2000FordFocus4Dwag
2005VolvoS604D
2003VolvoS604D
2003FordFoucs4Dwag
2001FordFocus4dwag
2004FordFocus4Dwag
2002FordFocus4DZX5
2001FordMustang2DGT
2000FordEscort4DSedan
2004VolvoS404D
2003FordFocusZX3
2005FordFocusZX32D
2002FordFocus2DZX3
2004FordFoucs2DZX3
2003FordFocus2DZX3
2000FordFocus2DZX3
2001FordFocus2DZX3
2005FordMustangCoupe2DV6De
2003FordMustang2DGT
2000FordMustang2DGT
2004FordMustang2D
2002FordMustang2DGT
46
Preliminary Results
Unknown: 2004FordFocus4DZX5.dh1
300
250
200
Series1
150
100
50
0
2003FordFocusZX3
2005FordMustangCoupe2DV6Deluxe
2004FordMustang2D
2005FordFocusZX32D
2002FordMustang2DGT
2000FordMustang2DGT
2001FordFocus2DZX3
2003FordMustang2DGT
2002FordFocus2DZX3
2004FordFoucs2DZX3
2001FordMustang2DGT
2003FordFocus2DZX3
2000FordFocus2DZX3
2000FordEscort4DSedan
2002FordFocus4DZX5
2004FordCrownVictoriaSedan4D
2005FordCrownVictoria4D
2003FordCrownVictoria4D
2001FordCrownVictoria4D
2000FordCrownVictoria4D
2002FordCrownVictoria4D
2004FordCrownVictoria4DLX
2005FordTaurusSE4D
2000FordContour4D
2002FordEscort4DSedan
2000FordTaurus4DSE
2003FordTaurus4dSES
2005FordFiveHundred4DSE
2001FordEscort4DSedan
2003FordFocus4DZTS
2005FordFocusZX54D
2003FordFocus4DSE
2004FordFocus4DZTS
2001FordTaurus4DSES
2002FordFocus4DZTS
2002FordTaurus4DSES
2002FordFocus4DSE
2001FordFocus4DLX
2004FordTaurus4DSE
2001FordFocus4DSE
2000FordFocus4Dwag
2004FordFocus4DZTW
2003FordFoucs4Dwag
2002FordFocus4Dwag
2004FordFocus4Dwag
2001FordFocus4dwag
2000FordFocus4DLX
2004FordFocus4DSVT
2003FordFocus4DZX5
2004FordFocus4DZX5
47
Preliminary Results
Unknown: 2004FordFocus4DZX5.dh1-Volvos Added to Known DB
300
250
200
Series1
150
100
50
0
2003FordFocusZX3
2005FordMustangCoupe2DV6D
2004FordMustang2D
2005FordFocusZX32D
2002FordMustang2DGT
2000FordMustang2DGT
2001FordFocus2DZX3
2003FordMustang2DGT
2002FordFocus2DZX3
2004FordFoucs2DZX3
2001FordMustang2DGT
2003FordFocus2DZX3
2000FordFocus2DZX3
2004VolvoS404D
2000FordEscort4DSedan
2002FordFocus4DZX5
2004FordCrownVictoriaSedan4D
2005FordCrownVictoria4D
2003FordCrownVictoria4D
2001FordCrownVictoria4D
2000FordCrownVictoria4D
2002FordCrownVictoria4D
2003VolvoS604D
2004FordCrownVictoria4DLX
2003VolvoS804D
2005FordTaurusSE4D
2000VolvoS704D
2000FordContour4D
2002FordEscort4DSedan
2002VolvoS604D
2000VolvoS804D
2001VolvoS804D
2004VolvoS604D
2005VolvoS804D
2001VolvoS404D
2001VolvoS604D
2000FordTaurus4DSE
2003FordTaurus4dSES
2005FordFiveHundred4DSE
2001FordEscort4DSedan
2003FordFocus4DZTS
2005FordFocusZX54D
2002VolvoS804D
2003FordFocus4DSE
2005VolvoS604D
2004VolvoS804D
2000VolvoS404D
2004FordFocus4DZTS
2001FordTaurus4DSES
2002FordFocus4DZTS
2003VolvoS404D
2002FordTaurus4DSES
2002FordFocus4DSE
2001FordFocus4DLX
2004FordTaurus4DSE
2001FordFocus4DSE
2000FordFocus4Dwag
2004FordFocus4DZTW
2005VolvoS404D
2003FordFoucs4Dwag
2002FordFocus4Dwag
2004FordFocus4Dwag
2001FordFocus4dwag
2000FordFocus4DLX
2004FordFocus4DSVT
2003FordFocus4DZX5
2004FordFocus4DZX5
48
Preliminary Results
Unknown: 2004FordTaurus4DSES.dh1
300
250
200
Series1
150
100
50
0
2003FordFocusZX3
2005FordFocusZX32D
2005FordMustangCoupe2DV6Deluxe
2002FordMustang2DGT
2004FordMustang2D
2003FordMustang2DGT
2000FordMustang2DGT
2004FordFoucs2DZX3
2001FordFocus2DZX3
2002FordFocus2DZX3
2003FordFocus2DZX3
2000FordFocus2DZX3
2001FordMustang2DGT
2002FordFocus4DZX5
2000FordEscort4DSedan
2004FordCrownVictoriaSedan4D
2004FordFocus4DZTW
2003FordCrownVictoria4D
2003FordFoucs4Dwag
2001FordCrownVictoria4D
2000FordCrownVictoria4D
2005FordCrownVictoria4D
2002FordCrownVictoria4D
2004FordCrownVictoria4DLX
2002FordFocus4Dwag
2001FordFocus4dwag
2001FordEscort4DSedan
2005FordFocusZX54D
2000FordFocus4Dwag
2002FordEscort4DSedan
2004FordFocus4DZTS
2000FordContour4D
2004FordFocus4DSVT
2002FordFocus4DSE
2005FordFiveHundred4DSE
2003FordFocus4DZX5
2004FordFocus4DZX5
2003FordFocus4DSE
2004FordFocus4Dwag
2003FordFocus4DZTS
2002FordFocus4DZTS
2001FordFocus4DLX
2000FordFocus4DLX
2001FordFocus4DSE
2003FordTaurus4dSES
2000FordTaurus4DSE
2001FordTaurus4DSES
2005FordTaurusSE4D
2004FordTaurus4DSE
2002FordTaurus4DSES
49
Preliminary Results
Unknown: 2004FordTaurus4DSES.dh1-Volvos Added to Known DB
300
250
200
Series1
150
100
50
0
2003FordFocusZX3
2005FordFocusZX32D
2005FordMustangCoupe2DV6Delux
2002FordMustang2DGT
2004FordMustang2D
2003FordMustang2DGT
2000FordMustang2DGT
2004FordFoucs2DZX3
2001FordFocus2DZX3
2002FordFocus2DZX3
2003FordFocus2DZX3
2000FordFocus2DZX3
2001FordMustang2DGT
2004VolvoS404D
2002FordFocus4DZX5
2000FordEscort4DSedan
2004FordCrownVictoriaSedan4D
2004FordFocus4DZTW
2003FordCrownVictoria4D
2003FordFoucs4Dwag
2001FordCrownVictoria4D
2000FordCrownVictoria4D
2005FordCrownVictoria4D
2002FordCrownVictoria4D
2004FordCrownVictoria4DLX
2002FordFocus4Dwag
2001FordFocus4dwag
2001FordEscort4DSedan
2005FordFocusZX54D
2000FordFocus4Dwag
2002FordEscort4DSedan
2004FordFocus4DZTS
2000VolvoS704D
2000FordContour4D
2003VolvoS604D
2004FordFocus4DSVT
2002VolvoS604D
2002FordFocus4DSE
2001VolvoS604D
2004VolvoS604D
2005FordFiveHundred4DSE
2003FordFocus4DZX5
2004FordFocus4DZX5
2003FordFocus4DSE
2005VolvoS604D
2004FordFocus4Dwag
2003FordFocus4DZTS
2002FordFocus4DZTS
2001FordFocus4DLX
2000FordFocus4DLX
2003VolvoS804D
2000VolvoS804D
2001FordFocus4DSE
2001VolvoS804D
2002VolvoS804D
2001VolvoS404D
2005VolvoS804D
2005VolvoS404D
2003FordTaurus4dSES
2000VolvoS404D
2003VolvoS404D
2000FordTaurus4DSE
2004VolvoS804D
2001FordTaurus4DSES
2005FordTaurusSE4D
2004FordTaurus4DSE
2002FordTaurus4DSES
50
Preliminary Results
Unknown: 2005FordMustangCoupe2DV6Deluxe.dh1
300
250
200
Series1
150
100
50
0
2000FordCrownVictoria4D
2003FordCrownVictoria4D
2002FordCrownVictoria4D
2004FordCrownVictoriaSedan4D
2003FordFocus4DSE
2001FordCrownVictoria4D
2004FordCrownVictoria4DLX
2002FordFocus4DZTS
2000FordContour4D
2005FordCrownVictoria4D
2001FordEscort4DSedan
2001FordFocus4DLX
2000FordFocus4DLX
2003FordFocus4DZTS
2000FordTaurus4DSE
2002FordEscort4DSedan
2001FordFocus4DSE
2001FordTaurus4DSES
2004FordFocus4DZTW
2004FordFocus4DZTS
2003FordTaurus4dSES
2004FordFocus4DSVT
2002FordFocus4DSE
2004FordFocus4DZX5
2002FordFocus4Dwag
2002FordTaurus4DSES
2003FordFoucs4Dwag
2003FordFocus4DZX5
2004FordTaurus4DSE
2001FordFocus4dwag
2000FordFocus4Dwag
2005FordTaurusSE4D
2004FordFocus4Dwag
2005FordFiveHundred4DSE
2005FordFocusZX54D
2001FordMustang2DGT
2002FordFocus4DZX5
2000FordEscort4DSedan
2003FordMustang2DGT
2000FordMustang2DGT
2002FordFocus2DZX3
2003FordFocus2DZX3
2000FordFocus2DZX3
2002FordMustang2DGT
2004FordFoucs2DZX3
2001FordFocus2DZX3
2005FordFocusZX32D
2003FordFocusZX3
2004FordMustang2D
2005FordMustangCoupe2DV6Deluxe
51
Preliminary Results
Unknown: 2005FordMustangCoupe2DV6Deluxe.dh1-Volvos Added to Known DB
300
250
200
Series1
150
100
50
0
2000FordCrownVictoria4D
2003FordCrownVictoria4D
2002FordCrownVictoria4D
2004FordCrownVictoriaSedan4D
2003FordFocus4DSE
2000VolvoS704D
2001FordCrownVictoria4D
2004FordCrownVictoria4DLX
2001VolvoS404D
2002FordFocus4DZTS
2000FordContour4D
2005FordCrownVictoria4D
2003VolvoS804D
2003VolvoS404D
2001FordEscort4DSedan
2001VolvoS804D
2001FordFocus4DLX
2000FordFocus4DLX
2000VolvoS404D
2003FordFocus4DZTS
2000FordTaurus4DSE
2002FordEscort4DSedan
2000VolvoS804D
2001FordFocus4DSE
2002VolvoS804D
2005VolvoS804D
2001FordTaurus4DSES
2004FordFocus4DZTW
2004FordFocus4DZTS
2003FordTaurus4dSES
2004FordFocus4DSVT
2002FordFocus4DSE
2004FordFocus4DZX5
2002FordFocus4Dwag
2001VolvoS604D
2004VolvoS804D
2002FordTaurus4DSES
2003FordFoucs4Dwag
2003FordFocus4DZX5
2004FordTaurus4DSE
2004VolvoS604D
2003VolvoS604D
2005VolvoS604D
2001FordFocus4dwag
2000FordFocus4Dwag
2002VolvoS604D
2005FordTaurusSE4D
2005VolvoS404D
2004FordFocus4Dwag
2005FordFiveHundred4DSE
2005FordFocusZX54D
2001FordMustang2DGT
2002FordFocus4DZX5
2000FordEscort4DSedan
2004VolvoS404D
2003FordMustang2DGT
2000FordMustang2DGT
2002FordFocus2DZX3
2003FordFocus2DZX3
2000FordFocus2DZX3
2002FordMustang2DGT
2004FordFoucs2DZX3
2001FordFocus2DZX3
2005FordFocusZX32D
2003FordFocusZX3
2004FordMustang2D
2005FordMustangCoupe2DV6Deluxe
52
Preliminary Results
Unknown: 2005FordTaurusSE4D.ws2
300
250
200
Series1
150
100
50
0
2003FordFocusZX3
2005FordFocusZX32D
2005FordMustangCoupe2DV6Delu
2004FordFoucs2DZX3
2002FordFocus2DZX3
2001FordFocus2DZX3
2003FordFocus2DZX3
2002FordMustang2DGT
2000FordFocus2DZX3
2003FordMustang2DGT
2000FordMustang2DGT
2004FordMustang2D
2001FordMustang2DGT
2002FordFocus4DZX5
2000FordEscort4DSedan
2004FordFocus4DZTW
2001FordCrownVictoria4D
2003FordFoucs4Dwag
2003FordCrownVictoria4D
2002FordCrownVictoria4D
2004FordCrownVictoria4DLX
2002FordFocus4Dwag
2000FordCrownVictoria4D
2000FordFocus4Dwag
2001FordEscort4DSedan
2001FordFocus4dwag
2004FordCrownVictoriaSedan4D
2004FordFocus4DZTS
2002FordEscort4DSedan
2000FordContour4D
2004FordFocus4DSVT
2003FordFocus4DZX5
2005FordCrownVictoria4D
2002FordFocus4DSE
2004FordFocus4DZX5
2004FordFocus4Dwag
2001FordFocus4DLX
2002FordFocus4DZTS
2003FordFocus4DZTS
2005FordFiveHundred4DSE
2003FordFocus4DSE
2005FordFocusZX54D
2001FordFocus4DSE
2000FordFocus4DLX
2001FordTaurus4DSES
2003FordTaurus4dSES
2000FordTaurus4DSE
2004FordTaurus4DSE
2002FordTaurus4DSES
2005FordTaurusSE4D
53
Preliminary Results
Unknown: 2005FordTaurusSE4D.ws2-Vovlos Added to Known DB
300
250
200
Series1
150
100
50
0
2003FordFocusZX3
2005FordFocusZX32D
2005FordMustangCoupe2DV6Deluxe
2004FordFoucs2DZX3
2002FordFocus2DZX3
2001FordFocus2DZX3
2003FordFocus2DZX3
2002FordMustang2DGT
2000FordFocus2DZX3
2003FordMustang2DGT
2000FordMustang2DGT
2004FordMustang2D
2001FordMustang2DGT
2004VolvoS404D
2002FordFocus4DZX5
2000FordEscort4DSedan
2004FordFocus4DZTW
2001FordCrownVictoria4D
2003FordFoucs4Dwag
2003FordCrownVictoria4D
2002FordCrownVictoria4D
2004FordCrownVictoria4DLX
2002FordFocus4Dwag
2000FordCrownVictoria4D
2000FordFocus4Dwag
2001FordEscort4DSedan
2001FordFocus4dwag
2004FordCrownVictoriaSedan4D
2004FordFocus4DZTS
2000VolvoS704D
2001VolvoS604D
2002FordEscort4DSedan
2000FordContour4D
2004FordFocus4DSVT
2003FordFocus4DZX5
2005FordCrownVictoria4D
2002FordFocus4DSE
2004FordFocus4DZX5
2005VolvoS604D
2004FordFocus4Dwag
2004VolvoS604D
2002VolvoS604D
2001FordFocus4DLX
2002FordFocus4DZTS
2003FordFocus4DZTS
2003VolvoS604D
2005FordFiveHundred4DSE
2003FordFocus4DSE
2005FordFocusZX54D
2001FordFocus4DSE
2000FordFocus4DLX
2000VolvoS404D
2000VolvoS804D
2001VolvoS404D
2005VolvoS404D
2001VolvoS804D
2003VolvoS404D
2003VolvoS804D
2001FordTaurus4DSES
2002VolvoS804D
2005VolvoS804D
2003FordTaurus4dSES
2000FordTaurus4DSE
2004FordTaurus4DSE
2004VolvoS804D
2002FordTaurus4DSES
2005FordTaurusSE4D
54
Known vs Known (as of Nov 12th)
2003FordMustang2DGT
254.185 250.633 109.297 249.126 108.027 254.113 251.303 259.786 243.888 245.835 242.243 242.393 241.877
2003FordTaurus4dSES
135.555 126.965
2002FordCrownVictoria4D
103.196 145.769 266.257 154.208 265.434 145.884
2002FordEscort4DSedan
116.005 137.942 259.742 146.017 257.911 137.572 118.731
2002FordFocus2DZX3
258.637 245.807
2002FordFocus4DSE
120.559 125.648 253.573 136.145 256.645 133.528 119.804 138.053 101.793
77.909 114.632 103.044 103.852 246.468 252.282 122.402 122.582 243.567 122.053 105.311 107.885 261.017 107.077 245.769 123.098 115.344 103.025 244.644
2002FordFocus4DZTS
123.436 132.637 257.362
95.894 122.939
2002FordFocus4DZX5
198.039 185.814 193.622 190.509
2002FordFocus4Dwag
145.92 126.825 249.754 133.968 254.776 142.713 136.715 159.707 108.293 115.939
2002FordMustang2DGT
252.749 253.154 115.616 249.333
2002FordTaurus4DSES
138.034 120.629 253.084 125.365 253.797
2001FordCrownVictoria4D
105.877 145.624 265.211 155.231 264.963 148.372
2001FordEscort4DSedan
113.581 142.068 257.541 146.427 260.258 143.145 107.663 130.771
2001FordFocus2DZX3
259.965 246.122
257.07 131.378 256.423 103.991 140.199 141.583
126.55 129.347
134.36 120.282 102.844
96.471
83.914
98.935
142.51 125.311
138.6 260.929 129.964 120.872 144.701 100.442
78.302 247.309
200.62 197.209
93.433
84.64
120.67 127.234 255.575 132.305 259.787 128.922 119.559
116.7
141.16 101.628
244.88
253.72
96.584 256.169
95.446 253.432 250.701 243.432
96.503 134.327 250.036 117.194 110.082 129.097
121.63
121.63 240.197
59.413 110.627
245.89 258.902
58.859 252.409 248.886 237.613 107.298 239.085
101.02 113.555 248.983 257.252 118.082 115.731 247.119
114.23
82.862 109.322 264.954 114.336
75.821
247.53 248.699
90.601
88.533 240.213 250.831 123.233 143.025 240.064 120.592 127.662
248.03 242.277 105.535
78.079 245.851 254.476 105.698
242.134 213.063 244.379 208.178 235.636 240.249
242.75
129.59
252.44 248.911
239.31 237.961
52.756 252.644 248.256 238.632 100.894 239.646
196.54 191.566
76.397 254.313
90.21 250.296 259.528 253.495
97.877 109.601 248.279 256.185 117.737 120.782 246.064
114.37
88.679 112.068 263.728
98.903 105.584 247.511 254.727 114.624 127.366 244.459
109.16
86.645 104.156 262.256 113.026 249.738 114.666 125.043 112.189
89.081
237.54 241.778 238.597 238.716 205.247 198.905 232.034
115.44 250.068 252.358
96.834 245.101 123.336
82.491 238.716
86.289 242.791 120.928 109.932 110.964 125.418 185.707 120.062 118.034 118.608 125.032 134.287 121.705 124.786 130.423 120.302 129.092 118.129 134.876 126.309
88.533 242.277 112.643 139.877 127.119 237.961 109.601 105.584
115.26 250.378 113.234 115.325 110.263 247.308
54.542 248.279 247.511 237.559 205.247 250.068 252.011 260.733 180.198
57.029 246.362 237.721
78.079 250.236 260.689 256.082 107.539 256.185 254.727 246.852 198.905 252.358 257.427 263.675 191.438 107.726 256.634 247.195
74.022 136.062 127.837 247.457 117.737 114.624
121.02 232.034
98.61 109.502 260.054 120.782 127.366 137.549
95.357 242.886 134.861 134.481 125.304 239.646
196.54 136.187
76.397 246.215 252.921 244.445
0
97.054
248.72 251.254 261.499 180.221
98.59 241.823 120.105 135.265
46.894 245.935 235.991
144.97 172.544 235.817 105.617
246.81
90.21 250.378 249.738 238.753 192.131 248.709 250.832 255.242 183.432
99.03 253.495 110.263 112.189 125.847 236.233 114.165 108.472 114.508
63.39 247.308
246.81 237.591 208.367 250.063 251.167
260.2 179.686
55.927 246.183 237.501 104.865 252.938 244.345 243.724 253.856 183.192 248.881
193.28 185.406
195.87 185.257 180.887 180.802 178.444 167.249 260.893 188.247 190.578 196.504
0 247.954 124.154 139.139 123.242
193.28 247.954
240.59 119.026
0 248.889 254.634 248.929 104.419
0 139.078
195.87 139.139 254.634 139.078
95.798 107.142 185.257 123.242 248.929
252.72
115.78
252.68 241.289 193.691
128.81 247.562 119.405 115.554 126.239 232.557
128.81 102.576
69.019 180.802 119.026
252.72 119.405 119.407 105.013 248.686
0
79.566 178.444
252.68 115.554 129.368 110.792 247.067
77.55
77.55 105.867 235.369 110.645 105.268
0 108.385
87.677 241.289 126.239 133.164 119.436 237.736 105.867 108.385
236.68 234.904 260.893 240.793 193.691 232.557
249.72
242.06 235.053 204.352 235.369
234.22 107.418 111.678
57.034 247.419 238.534
0
98.423
2000FordTaurus4DSE
140.154 125.609 259.223 137.202 258.295
253.55 252.555
91.873 137.231 251.729 118.983 124.483 123.458 263.481 140.966 253.757 100.392 138.951 130.477 252.938
130.37 121.476 191.263 135.152 253.189
91.051
2005VolvoS404D
139.568 108.818
108.94 109.932 242.512 245.697
92.815 143.699 243.968 113.065 119.475 110.802 254.774 131.535 243.202 103.697 136.726 134.506 244.345
124.29 119.451 180.523 118.119 246.443
91.142 141.857 131.614 243.069 116.239
2005VolvoS604D
114.853 131.146 251.332 131.953 251.525 131.138 112.934 132.239 107.195 105.576 123.934 110.488 110.964 243.536 248.713 113.907 123.538
2005VolvoS804D
131.257 122.848
2004VolvoS404D
203.158
2004VolvoS604D
109.743 136.232 253.497 134.602 253.439 129.768 115.864
2004VolvoS804D
132.665 121.666 256.946 129.588 256.318 101.555 136.978 143.441 121.793 122.669
2003VolvoS404D
134.759 122.998 259.452 135.124 261.371 108.446 129.094 142.877 122.418 124.086 126.276 118.477 118.608
2003VolvoS604D
108.281 135.307 254.441 135.967 251.386 129.381 114.761 131.203 118.334 106.241 142.173 128.289 125.032 248.957 248.194 125.749 119.556 248.211 144.338 120.128 136.251
2003VolvoS804D
137.478 135.795 262.649 144.006 260.925 117.179 143.779 143.962 138.412 133.004 150.106 138.319 134.287 256.576 257.205 109.328 144.085 257.215 107.338 123.244
2002VolvoS604D
115.765 137.754 254.006 131.602 250.662 127.275 116.341 129.589 118.825 121.398 136.539 122.878 121.705 248.365 246.729 119.084 126.832 248.541 143.352 128.932 137.072
258.16 132.326
2002VolvoS804D
119.794 126.436 259.622 129.777 258.429 112.737
264.21 133.727 250.474 107.067 120.104 120.512 251.881
98.512 140.734 148.023 125.431
133.33 140.309 123.336 120.928
249.19 115.675 249.251 105.521 138.868 148.277 117.111 131.297 125.731
260.64 136.571 258.306 107.819 135.753 139.967 127.206 122.855
194.59 190.601 197.758 191.282
133.11 122.952
103.68 255.056
145.44 128.956 125.418 255.001 254.834 103.039 137.649 253.598 107.402 110.377
134.06 119.575 120.062
109.92 118.231 164.587
69.287 246.602 253.937 247.388
129.61 265.075 141.959 254.605 102.798 132.605 123.477 253.856 116.552 112.507 194.814 139.637 255.142 105.337
252.75 255.853
95.405 139.664 250.064 106.992 111.167 124.838 263.196 134.866 250.051
98.952 134.356 127.853
250.34 115.648 112.052 189.964 131.144
251.91
89.416 130.383 252.315 104.795 120.119 118.228 265.924 130.783 250.719 102.934 127.896 126.473 252.677 118.918 110.887 185.965 122.959 253.067
257.82 130.351 246.346 126.967 113.985 102.236
143.07 267.242
115.76 124.786 253.584 254.148 105.744 126.922 252.673 114.359 108.278 126.805
93.925 137.891 127.001 250.838
110.46
0
0
192.51
0
79.567 120.648
191.94
231.12 103.514 118.123 125.766 185.245 250.433
112.64
113.63 123.025
132.8
98.488 125.251 249.419
94.601
113.35 128.594
248.8 133.557 131.062 244.198 133.519 118.659
140.47 252.721 102.522
129.28 247.674 130.485 121.207
2001VolvoS804D
132.043 130.283 264.453 143.188 261.362
2000VolvoS404D
134.073 131.215 258.266 133.489 259.834 105.623 131.724 142.322 122.008 123.792 134.135 121.766 118.129 252.189 252.962
2000VolvoS704D
116.462 143.599 264.866 149.439 265.884 138.436 110.593 129.603 120.475 110.508 137.509 126.692 134.876 258.149 261.667 129.656 108.974 256.925 130.427
2000VolvoS804D
137.533 125.878 260.986 139.069 258.365 114.913 143.755 144.485 134.051 129.795 147.934 135.003 126.309
255.34
122.6 257.576 121.282
114.45 136.117 269.766 131.687
254.67 107.246 143.059 254.573 107.367 117.258
254.98 123.734 103.019 114.793 256.581 112.539
137.97 265.497 145.036 253.958
118.1 119.567 234.143 120.049 108.958 117.125 189.006 249.228 132.634
116.28 196.753 143.672 253.035 107.218 135.882 132.231 258.131 116.593 115.777 137.268 230.627 107.568
131.34 252.207 117.098 119.875 125.153 264.962 134.456 249.801 104.451 131.101 124.025 253.513 119.237 111.002 190.915 130.305 249.689
93.127 133.748 122.129 253.553 113.492
97.594 194.904 137.951 255.861 133.284 109.717 110.332 258.856
106.26 138.149 125.836 255.608 125.377 120.622 196.225 142.665 252.324 106.224
111.56 124.632 228.986
94.796 108.182 127.514
125.54 135.952 198.457
256.38
96.067
139.14 135.504 255.604 119.683 117.015 138.174 229.169 107.955 131.047
98.664 194.584 255.413
115.26
126.7
192.51 120.648
113.63
112.64 122.278 106.945 105.831 120.049 107.568
132.8 119.451 113.496 115.694 108.958
88.814
248.8 252.162 254.656 249.228
99.626 128.844
78.912 102.241
0
119.66
124.81
91.503
91.522 124.153
95.058 124.804
0
132.1
98.664
140.75
140.47
256.38 251.267 255.413 255.031
119.88 113.676
128.28 116.374
129.28 143.925 130.743 134.246 142.607
89.786
98.732 105.686 119.611 109.494 192.066 123.369 100.848
80.053 197.382 121.888
85.794 106.198
84.643
83.597 109.494 119.702
132.1 108.847 131.234 102.772
86.707 126.424
99.884
0 130.935
94.092 119.181 128.205
0 128.868 100.658 105.278
94.092 128.868
94.086
127.76
0 117.668 130.428
127.76
92.195 125.515 101.307
85.794
82.913 119.155 106.198
126.88 122.361
125.33
92.195 111.343 128.146
83.639
86.832 125.515 124.028 129.361 127.368
0 126.626
86.832 113.958 126.626
83.639 127.368
94.086 122.297
0 102.683 113.958 101.307 110.524
126.88 111.343 124.028 110.524
125.33
80.053
123.44 123.369 121.004 121.888
89.786 100.848 123.931
99.884 122.297
82.913
99.73 118.752
190.35 195.694 191.709 196.672 192.066 201.557 197.382
86.707 128.205 105.278 130.428 102.683
84.643 120.162 126.424
77.431 124.645 119.611 123.043 125.786
98.648 120.162
90.385 102.772 119.181 100.658 117.668
98.648
88.814
126.7
89.077 114.207 128.077
92.508 118.706
98.732 133.625 108.042
115.26 121.207 113.541 105.686 134.521 110.738
90.385
92.508 108.847 130.935
89.077 118.706 131.234
123.44
78.957 113.487
192.89 192.631 202.383 194.306
95.058
99.73 195.694 128.077
113.35 118.659 103.222
94.869 128.594
128.28 134.246 255.259 133.625 134.521 123.043 119.702 201.557 121.004 123.931 119.155 122.361 128.146 129.361
140.75 197.027 255.031 116.374 142.607 254.004 108.042 110.738 125.786
96.067 131.047
189.76 192.875 189.006 198.457 190.326 194.584 197.027
94.601 128.672 113.436 133.519 116.028 102.522 130.485 103.889
91.522 124.804
190.35 114.207
83.597 196.672
239.67 229.169
97.814 125.692 107.955
125.54 118.774
137.26 125.286 118.442 127.171 117.125 135.952 129.829
191.84 197.084 188.898
98.488 140.064 121.394 133.557 112.535 120.468 132.634
0 117.857 128.431
91.503 202.383 124.153
124.81 194.306
96.214
191.94 189.034
192.89 128.431
119.66 192.631
77.431 118.752 191.709
119.88 143.925 254.783 103.889 113.541 124.645
97.814 118.774 129.829 190.326 251.267 113.676 130.743 248.985
239.67 125.692
78.957
189.76 252.162 112.535 132.155 250.692 116.028 103.222 113.487
134.19 137.555 263.666 143.532 262.547 109.823 131.254 141.008 128.343 123.283 142.092 128.863 130.423 257.016 255.872 102.362 127.681 256.451 112.089 116.975 130.514 267.976 142.515 254.233 105.984 128.801 120.939 256.571 120.149 111.959 194.555 138.871 253.365 106.766 133.367 122.203 257.352 116.534 109.856 136.112 230.468 105.831 115.694 127.171 192.875 254.656 120.468
90.023
94.869
94.796 119.683
111.56 108.182 117.015
231.12 233.919 227.408 231.965 232.071 230.468 234.143 230.627 228.986
79.567 114.388 123.776
78.912 189.034 117.857
99.626 123.776 102.241
191.84 248.496 140.064 132.904 246.942 128.672 128.844
137.26 197.084 257.117 121.394 147.787 253.893 113.436
99.203 127.675 232.071 106.945 113.496 118.442
96.214 114.388
110.315 132.187 256.627 137.607 255.121 133.779 111.632 125.729 115.016 108.771 134.012 119.397 120.302 249.648 251.846 120.878 121.997 248.974 137.062 122.697 132.838 262.383 128.596 246.139 122.138 107.319 105.979 249.343
258.01 255.888 105.103 135.858 256.814 111.552 117.643 137.309 268.768 143.823 255.241 102.782 133.012
0 122.051 189.171
254.4 107.695 106.179 122.051
246.85 132.676 119.896
118.1 115.777
246.85 250.793 249.419 246.942 253.893 244.198 250.692 252.721 247.674 254.783 248.985 255.259 254.004
0 116.226 106.179 184.308 119.896
2001VolvoS604D
112.22 137.999 141.344 129.514 126.692 147.548 132.567 129.092
254.4 184.341
104.08 129.495 107.695 194.873 132.676 108.033
2001VolvoS404D
103.65 115.992 189.539 131.501 245.822 122.006 104.641 109.221 249.419 107.594
231 231.851 170.452 231.617 230.781
190.63 170.452 190.353 199.419 207.095 238.356 179.958 194.734 192.479 184.341 194.873 184.308 189.171
103.16 128.858 230.781 100.199 126.141 134.189 189.599 249.246 103.647 131.434 250.793 108.033
257.57 124.222 122.894 143.074 227.408
100.92
99.203 109.856
0 252.169 242.633 243.633
104.08
244.21 125.527 118.607 119.162 248.827 116.547 127.942 190.977 133.592 242.556 121.513 116.232 118.418 248.692 121.597 127.112 123.451 231.965 122.278 119.451 125.286 188.898
111.58 103.874 190.303 129.673 253.174 106.057 125.959 116.997 253.255
100.92 116.534 107.594 116.593 113.492
103.16 107.738 120.131 122.894 127.112
0 242.079 136.286 122.307 120.311 138.898 192.479 128.956 131.434 125.251 132.904 147.787 131.062 132.155
97.364 130.148 141.315 189.061 251.927 117.293 136.286 252.169
92.803 132.095 121.663 253.632 111.564 107.738 122.811
150.47 254.599 108.729 138.632 132.779 257.229 130.936 124.667 197.806 145.495 255.297 107.368 141.479 135.314
110.46 111.564 115.595 124.222 121.597
231 115.679 110.124 121.206 181.921 243.412 125.257 120.311 243.633 129.495 116.226
249.98 111.041 118.655 192.712 139.879 245.333 127.973 114.937 114.739 249.401 115.595 120.131 126.397 233.919 121.079
139.14
190.63 119.777 128.858 122.811 126.397 143.074 123.451 127.675 136.112 119.567 137.268 124.632 127.514 138.174
97.364 103.964 115.679 104.523 190.353 119.938 100.199 103.514 121.079
114.85 121.806 231.323 103.964 131.859 137.779 177.418 243.361 100.362 122.307 242.633
187.99 185.822
93.127 133.284 106.224
187.99 109.903
0 112.063 247.805 117.293 100.362 125.257 113.261 194.734 133.788 103.647
132.12 129.828 255.109 110.878 105.279 136.724 231.851 104.523 124.426 133.038 194.387 253.148 113.261 138.898
196.55 206.244 196.366 183.192 190.097 189.368 239.203 194.782 184.153 190.167 208.651 199.026 182.603
97.594 120.622
257.57 248.692 253.255 257.352 249.419 258.131 253.553 258.856 255.604
0 245.073 235.402 100.842 251.927 243.361 243.412 253.148 179.958 247.667 249.246 250.433 248.496 257.117
78.287 242.778 132.302 136.207 141.951 170.992 235.402 112.063
142.61 139.576 253.805 123.575 122.401 135.172 234.247
92.803 127.973 107.368 121.513 106.057 106.766 122.006 107.218
0 178.476 178.445 170.992 183.993 189.061 177.418 181.921 194.387 238.356 189.688 189.599 185.245
99.486 251.893 251.422 240.587 189.266 249.139 252.103 254.495 183.993 100.842 247.805 242.079
99.166 114.637 182.978 121.904 243.733 114.043 112.264 104.258 243.966 108.121 112.993 111.201
93.925
0 193.879 259.611 134.943 141.951 254.495 141.315 137.779 121.206 133.038 207.095 119.637 134.189 125.766 123.025
57.034 246.021 243.971 235.974 206.637 249.613 249.531 259.611 178.476
85.26 244.409 128.888 137.892 129.397 238.534 112.526 111.193
66.089 252.819 253.125 250.173 104.865 248.623 252.249 188.225 242.253
191.99 187.107 198.481 195.943 183.506
99.813
116.28 111.002
114.85 112.993 105.279 185.822 116.077
78.287 240.587 135.172 121.806 111.201 136.724
247.47 250.063 121.717 123.098 247.328 136.531 119.854 133.987 259.392 130.056 245.696 121.669 115.352 110.064 248.881 104.018 117.074 189.491 132.351 246.278 121.677 112.539 108.587 248.388 109.903 116.077 119.777 231.617 119.938 109.456 119.637 189.688 247.667 133.788 128.956
136.79 122.493 118.034 250.389 252.463
124.57 130.835 117.285 114.291 133.772
78.318 241.306 139.581 139.531 134.721 237.501
124.72 135.502 241.497 135.783
71.019 171.218 190.724 184.418 193.412 182.115 181.245 184.765 183.439 173.241 258.675 188.756 186.979 193.879
55.927 242.748 246.758 178.257 239.053 106.166 246.107 256.365 247.805
242.81 133.922 119.262 125.755 258.431 120.786 242.033 120.486 114.526 110.533 243.724
193.55 205.675 215.788 186.813 193.982 198.754 184.521 185.707 182.543 184.347 188.226 206.591 180.389 202.382
130.05 115.397 107.655
94.613 254.137
252.82 244.925 122.205 242.719
116.02
103.65 121.282 119.237 112.539 125.377
251.91 253.067 245.333 255.297 242.556 253.174 253.365 245.822 253.035 249.689 255.861 252.324
99.813 186.979 249.531 130.974 136.207 252.103 130.148 131.859 110.124 124.426 199.419 109.456 126.141 118.123
253.256 251.446 113.757 249.261 105.029 252.644 251.053 259.184 246.419 248.774 243.462 245.101 242.791
98.962 261.755
111.58 120.149
132.12 208.651 112.539 137.891 132.095 114.937 141.479 116.232 125.959 133.367 104.641 135.882 133.748 109.717
99.486 253.805 243.069 243.966 255.109 182.603 248.388 250.838 253.632 249.401
0 123.563 135.783 188.756 249.613 113.335 132.302 249.139
141.484 123.437 245.595 130.664 251.436 141.212 132.208
96.022
91.142 114.043 105.337 190.167 121.677
142.61 141.857 112.264
0 232.776 236.574 241.497 258.675 206.637 238.143 242.778 189.266 234.247 231.323
89.132 137.251 126.197 250.353 110.645 107.418 127.523 232.776
119.62 117.611 196.504 136.471 256.473 137.464
91.051
124.72 183.439 243.971 106.247 111.193 251.422 122.401
143.151 122.758 255.371 127.199 259.978 126.283 137.179 155.647 111.331 122.948 113.477 106.176 113.563 246.362 256.634 102.355 141.245 245.935 104.211 117.185 105.617 263.674 116.789 246.322 118.348 137.408 134.785 246.183 116.499 111.494 176.196 109.739 250.612 106.054 138.942 124.019 247.419 110.292 106.247 111.007 238.143 113.335 130.974 134.943 178.445 245.073
93.703 249.844 257.716 251.268
249.98 257.229 248.827 251.881 256.571 249.343 257.576 253.513 256.581 255.608
99.166 116.552 190.097 104.018 115.648 118.918 111.041 130.936 116.547
116.02 184.765 246.021 110.292 112.526 251.893 123.575 116.239 108.121 110.878
2000FordMustang2DGT
260.2
189.51 179.686 181.983 185.922
124.29
98.962 182.115 247.805 124.019 129.397 247.388 139.576 131.614 104.258 129.828 199.026 108.587 127.001 121.663 114.739 135.314 118.418 116.997 122.203 109.221 132.231 122.129 110.332 135.504
0 238.386 127.523 128.096 135.502 173.241 235.974 111.007
234.22 238.386
130.37
69.287 253.189 246.443 243.733 255.142 184.153 246.278
96.022 193.412 256.365 138.942 137.892 253.937
0 248.686 247.067 237.736 204.352 250.353 252.932 261.755 181.245
90.468
106.26
122.6 124.025 114.793 125.836
85.26 242.253 135.152 118.119 121.904 139.637 194.782 132.351 131.144 122.959 139.879 145.495 133.592 129.673 138.871 131.501 143.672 130.305 137.951 142.665
89.132 126.919 137.464 184.418 246.107 106.054 128.888 246.602
0 249.515 105.013 110.792 119.436 235.053 126.197 103.854
92.372
109.92 248.623
249.72 253.802 256.473 190.724 106.166 250.612 244.409
242.06 137.251 110.545
254.98 253.958
71.019 178.257 176.196 164.587 188.225 191.263 180.523 182.978 194.814 239.203 189.491 189.964 185.965 192.712 197.806 190.977 190.303 194.555 189.539 196.753 190.915 194.904 196.225
87.677 240.793 128.669 135.197 136.471 171.218 239.053 109.739
0 102.576 259.533 119.407 129.368 133.164
240.59 104.419 247.562 259.533 249.515
115.78
119.62 181.983 242.748 116.499
250.34 252.677
79.566 110.712 234.904 107.575 107.192 117.611 185.922 246.758 111.494 118.231 252.249 121.476 119.451 114.637 112.507 189.368 117.074 112.052 110.887 118.655 124.667 127.942 103.874 111.959 115.992
2000FordFocus4Dwag
82.868 114.508
244.21 250.474 254.233 246.139 255.241 249.801
98.952 102.934 126.967 108.729 125.527 107.067 105.984 122.138 102.782 104.451 123.734
90.468 100.245
0 167.569
236.68 117.929 103.994
137.97
264.21 267.976 262.383 268.768 264.962 269.766 265.497
150.47 132.326 133.727 142.515 128.596 143.823 134.456 131.687 145.036
66.089 253.757 243.202 242.033 254.605 183.506 245.696 250.051 250.719 246.346 254.599
196.55 121.669
258.16
69.019
2000FordFocus4DLX
144.97 273.591 140.382 255.242 137.536
78.318 242.719 140.966 131.535 120.786 141.959 195.943 130.056 134.866 130.783 130.351
114.45 117.258
143.07 137.072 126.805 130.514 132.838 137.309 125.153 136.117
257.82 267.242
92.372
83.879 247.424 101.739 252.684
84.27 244.965 254.069
191.99 119.854 111.167 120.119 120.128 123.244 128.932 108.278 116.975 122.697 117.643 119.875
129.61 187.107 133.987 124.838 118.228 136.251
95.798 246.078
257.432 245.094
86.289 237.721 247.195 124.723 141.152 235.991
94.613 244.925 123.458 110.802 125.755
189.51 251.268 134.785 134.721 250.173 130.477 134.506 110.533 123.477 196.366 110.064 127.853 126.473 102.236 132.779 119.162 120.512 120.939 105.979
2000FordFocus2DZX3
96.834
242.81 253.598 180.389 247.328 250.064 252.315 248.211 257.215 248.541 252.673 256.451 248.974 256.814 252.207 256.925 254.573
252.82 124.483 119.475 119.262 110.377
121.98 252.565 121.745 121.362 107.142 249.584
93.343 134.082 114.165 250.063 117.929 107.575 188.247 128.669
184.37 172.988 174.157 172.956 180.198 191.438 182.664 196.759 180.221 188.021 188.368 172.544 201.343 175.969 183.432 191.959 193.003
93.244
103.68 251.729 243.968
133.11 255.056 118.983 113.065 133.922 107.402 202.382 136.531 106.992 104.795 144.338 107.338 143.352 114.359 112.089 137.062 111.552 117.098 130.427 107.367
93.703 246.322 241.306
191.44 190.196 195.696 194.889 191.989 207.868 180.972
158.87 105.089 110.827
254.67
95.405
252.75 248.957 256.576 248.365 253.584 257.016 249.648
82.868 193.003 257.716 137.408 139.531 253.125 138.951 136.726 114.526 132.605 206.244 115.352 134.356 127.896 113.985 138.632 118.607 120.104 128.801 107.319 133.012 131.101 103.019 138.149
108.19 151.257 268.258 156.218 268.379 145.585
46.894 252.227 246.989 235.817 108.503 236.993
255.34
92.815 113.907 103.039 188.226 121.717
93.343 121.974 137.536 191.959 249.844 118.348 139.581 252.819 100.392 103.697 120.486 102.798
83.807 100.162 259.528 115.325 125.043 133.018 240.574 134.082 103.647
180.92 239.709 109.738 246.022 256.186 248.108
121.98 167.569
76.544 244.149 132.632 138.271 140.382 175.969 236.993 116.789
95.41 139.858 131.083 250.296 113.234 114.666 134.097 234.207
254.31 135.883
97.054 178.602 111.666 248.206 123.039 116.305
0 182.749
115.26 113.026
115.76 128.863 119.397 132.567 121.766 126.692 135.003
258.01 252.189 258.149
91.873
247.47 250.389
131.34 108.974 143.059
198.794 183.724
57.029 107.726 246.048 257.442
253.55 242.512 243.536 255.001 182.543
240.75 129.982 112.834 101.005 196.759 257.442 141.245 141.152 254.069 137.231 143.699 123.538 137.649 206.591 123.098 139.664 130.383 119.556 144.085 126.832 126.922 127.681 121.997 135.858
2000FordEscort4DSedan
254.6 266.396 241.004 244.276 240.628 238.155 236.182
136.79 126.276 142.173 150.106 136.539 133.772 142.092 134.012 147.548 134.135 137.509 147.934
90.023 129.656 107.246
120.55 111.106 134.062 122.641 131.506 252.011 257.427 126.552 112.834 251.254 130.969 120.305 135.265 266.008 138.271 250.832 121.974 103.647 108.472 251.167 103.994 107.192 190.578 135.197 253.802 126.919 110.545 103.854 252.932 105.268 111.678 128.096 236.574 123.563
124.76 128.715 117.899 135.975 131.222 141.375 260.733 263.675 135.496 101.005 261.499 136.092 132.323
134.06
84.27 252.555 245.697 248.713 254.834 184.347 250.063 252.463 255.853 248.194 257.205 246.729 254.148 255.872 251.846 255.888 252.962 261.667
89.636 126.552 135.496 182.664 246.048 102.355 124.723 244.965
2000FordCrownVictoria4D
97.768
98.423
145.44 198.754
108.94 110.488 128.956 184.521 119.575 122.493 118.477 128.289 138.319 122.878
89.416 125.749 109.328 119.084 105.744 102.362 120.878 105.103
52.756 246.064 244.459 235.783 206.539
98.322 247.646 120.285 142.049 131.237 238.632 112.068 104.156
250.38 186.213 240.735
76.544 238.753 134.097 133.018 125.847 237.591 100.245 110.712 167.249
248.72 116.988 105.397 120.105 262.118 132.632 248.709
115.44 131.506 141.375 172.956 236.182 113.563
86.645 118.279 234.763 105.397 120.305 132.323 188.368 246.989 117.185 122.952
63.39 246.078 249.584 180.887
240.75 206.539 232.013 234.763 241.823 217.518 244.149 192.131 234.207 240.574 236.233 208.367
89.636 129.982
133.33 131.297 105.576 122.855 193.982 107.655 122.669 124.086 106.241 133.004 121.398 114.291 123.283 108.771 126.692 123.792 110.508 129.795
93.244 243.462 140.309 125.731 123.934
97.877
109.16 131.115 232.013 116.988 130.969 136.092 188.021 252.227 104.211
180.92 178.602 182.749
99.03 248.108
87.464
240.31
184.37 244.276 122.948 110.827 248.774
89.081 238.597 114.482 122.641 131.222 174.157 238.155 106.176
239.31
88.679
83.807 115.662 256.186 116.305 121.362
120.84
98.59 254.828
120.55 128.715 180.972 241.004 111.331 105.089 246.419 125.431 117.111 107.195 127.206 186.813 115.397 121.793 122.418 118.334 138.412 118.825 117.285 128.343 115.016 129.514 122.008 120.475 134.051
237.54 121.504 111.106 117.899
114.37
254.31 248.334 109.738 248.206 252.565
94.961
121.02 137.549 235.783 131.115 118.279
240.31 115.166
259.49 115.805 144.152 124.133 252.644
0 244.644 248.455
112.22 105.623 138.436 114.913
124.57 131.254 111.632 137.999 131.724 110.593 143.755
130.05 143.441 142.877 131.203 143.962 129.589 130.835 141.008 125.729 141.344 142.322 129.603 144.485
98.903
248.03 113.198 134.618 117.532
95.41 135.883 132.452 246.022 123.039 121.745 185.406 124.154 248.889
95.621 120.458
82.491 237.559 246.852
193.55 129.768 101.555 108.446 129.381 117.179 127.275 112.737 109.823 133.779
158.87 259.184 148.023 148.277 132.239 139.967 215.788
90.601
0 252.582 103.025 108.573 191.566 137.153 248.334 132.452 115.662
250.38 116.097 117.255 108.573 248.455
98.61 258.077 144.152 135.766 142.049 270.812 134.481 252.921 139.858
54.542 107.539 247.457 260.054
97.075 245.769
95.357 240.735 137.297 136.187 137.153 239.709 111.666
259.49 254.099 247.646 121.911 242.886
95.88
94.961 103.329
124.76 207.868 266.396 155.647
82.862 186.241 127.662 254.099 125.475 135.766 117.415 248.256
0 111.496 257.419 115.344 117.255
97.075 250.398 257.419 252.582
74.022 142.768 246.249 115.805 125.475 120.285 259.845 134.861 246.215
95.621
98.512 105.521 131.138 107.819
254.6 137.179 132.208 251.053 140.734 138.868 112.934 135.753 205.675 115.864 136.978 129.094 114.761 143.779 116.341
129.59
87.464 241.778 134.642 134.062 135.975 172.988 240.628 113.477
0 135.686 125.918 250.398 123.098 116.097 192.835 137.297 254.313
252.44 135.686
97.169 119.945 117.532 127.119 248.699 256.082 127.837 109.502 249.058 124.133 117.415 131.237 264.925 125.304 244.445 131.083 100.162
256.64 104.243 135.858 147.186 115.166 121.504 134.642 114.482
259.37 147.359 261.516 135.778 114.574
98.322 257.669
116.7 243.047 101.628 104.283
97.169 246.712
97.768 191.989
242.75 147.186
261.48 271.354 263.741 107.298 261.017 264.954 203.953 257.669 121.911 259.845 270.812 264.925 100.894 263.728 262.256 254.828 217.518 262.118 266.008 273.591 201.343 108.503 263.674 254.137 122.205 263.481 254.774 258.431 265.075 198.481 259.392 263.196 265.924
0 251.733
261.48 143.387 251.733
141.16 147.638 154.045
120.84
114.23 186.673 120.592
0 240.197 143.387 137.773 129.653 239.085 107.077 114.336 167.608
97.096 259.224 138.943 132.005 142.188 271.354 137.773
113.85 130.771 267.705
58.859 243.567 247.119 178.972 240.064 105.698 246.249 258.077 249.058
96.584 243.432 129.097 142.188 134.395 237.613 107.885 109.322 166.989
0 256.169
95.88 103.329
123.25 254.452 130.321 258.593 122.685 127.214 147.638 104.283
119.868 143.909
94.387
95.446 250.036 259.224 251.762
134.19 110.315 132.043 134.073 116.462 137.533
194.59 136.232 121.666 122.998 135.307 135.795 137.754 126.436 137.555 132.187 130.283 131.215 143.599 125.878
260.64 190.601 253.497 256.946 259.452 254.441 262.649 254.006 259.622 263.666 256.627 264.453 258.266 264.866 260.986
256.64 261.516 268.379 195.696 101.739 259.978 251.436 105.029 258.295 249.251 251.525 258.306 191.282 253.439 256.318 261.371 251.386 260.925 250.662 258.429 262.547 255.121 261.362 259.834 265.884 258.365
84.64 107.663 257.636 119.559 127.214 130.112 240.249 135.858 114.574
247.53 128.873 113.573
245.89 122.402 118.082 183.582 123.233 245.851
262.22 122.131 250.701 110.082 132.005 111.298 248.886 105.311
0 255.166
262.22 255.166
138.6
97.096 108.289 258.902 122.582 115.731 198.128 143.025 254.476 142.768
138.17 127.109 128.061 253.347 254.083 130.136 108.289 251.762 135.651 111.298 134.395 263.741 129.653 248.911 125.918 111.496
96.807 252.716 257.636 267.705 243.047 246.712 241.332
135.917 127.681 246.147 130.337 251.382 141.597 130.112 154.045
132.677 127.827 256.933 131.685
90.982
125.52 250.724 255.569
137.1 130.758 139.241 258.874 260.998 134.775
113.85 129.055 113.573 139.653 134.618 139.877 258.571 260.689 136.062
127.405
2000FordContour4D
0 118.391
239.56 249.802 128.666 137.447 237.119 138.898 122.131
138.6 146.157 119.134 128.873 132.338 113.198 112.643 246.477 250.236
2001FordFocus4dwag
2001FordTaurus4DSES
246.93 235.993 106.995 237.119
96.807 259.787 258.593 251.382 208.178
97.699 148.372 143.145 252.716 128.922 122.685 141.597 235.636 104.243 135.778 145.585 194.889 252.684 126.283 141.212 252.644
59.413 246.468 248.983 180.816 240.213 105.535 246.477 258.571 248.699
0 116.269 124.636 268.487 138.898 253.432 117.194 138.943 135.651 252.409 122.053
246.93 116.269
192.82 209.924 174.657 181.019 169.054 167.822 168.013 180.816 196.382 183.582 198.128 178.972 186.673 186.241 166.989 203.953 167.608 186.213 192.835
100.9 252.308 252.106 259.623 245.627
97.699
0 252.796
117.87 145.377 252.796
256.33 122.512 122.066
90.094 238.857 248.858 115.737 142.232 235.993 124.636 118.391
84.33 248.359 102.878 253.806 255.283 266.377 241.636 245.557 241.458 239.087 238.047
2001FordFocus4DSE
240.4
94.085 102.645
90.333 119.773 126.034 114.536
2001FordFocus4DLX
2001FordMustang2DGT
91.639 112.225 104.552
107.97 245.631 258.576
2000VolvoS804D
138.376 130.888 248.652 134.839 253.409 148.216 129.403 158.842
57.552
125.91 251.969 261.238
253.72 134.327
2000VolvoS704D
2003FordFoucs4Dwag
73.236 250.562
127.31 122.998 121.424
112.99 105.788 130.711 112.546 116.776 248.072
0 258.576 145.377 122.066 142.232 270.424 137.447
96.503 134.775 130.136
249.19 251.332
75.821 248.699 132.338 139.653 119.945 241.332 120.458
94.387 255.569 260.998 254.083 110.627 252.282 257.252 196.382 250.831
244.88
83.879 255.371 245.595 113.757 259.223
2000VolvoS404D
89.901 256.844 268.762 271.133 259.105 260.509 259.367 257.363 252.033 103.202 112.941 259.631 270.424 106.995 268.487
255.43 138.073 258.337 129.207 134.826 151.269
256.33 248.858 112.941 249.802
117.87 122.512 115.737 259.631 128.666
191.44
2001VolvoS804D
252.98 130.211 137.747 161.967
267.088 251.891
145.03 136.622 142.235 259.534 261.427 138.838
82.394 247.388 101.606 252.671 256.361 267.555 240.455 244.623 240.877 237.795 235.282
107.97 261.238
0 138.838 245.631
108.19 198.794 257.432 143.151 141.484 253.256 140.154 139.568 114.853 131.257 203.158 109.743 132.665 134.759 108.281 137.478 115.765 119.794
259.37 268.258
2001VolvoS604D
148.084 117.238 248.067 126.115
2003FordFocusZX3
125.06 128.507 115.805
0 248.528 261.427
129.68 113.268 110.464 247.102 248.528
2001VolvoS404D
2003FordFocus4DZX5
93.225
126.31
101.02 167.822
125.52 139.241 128.061 238.047 103.852 113.555 168.013
90.982 250.724 258.874 253.347
2002VolvoS804D
130.956 130.778
99.249 133.915 146.022 112.391
239.56
2002VolvoS604D
148.632 128.734 261.152 145.601 264.743 128.868 145.553 155.109 132.698
2003FordFocus4DZTS
87.886 247.506 259.374 262.564 252.077 252.733 252.309 250.919 243.518 107.814
57.552 251.969 248.072 238.857 103.202
2003VolvoS804D
258.794 246.205
2003FordFocus4DSE
0 107.814 247.102 259.534
93.433 241.877
2003VolvoS604D
2003FordFocus2DZX3
238.92 237.736
90.094 252.033
2003VolvoS404D
105.532 152.196 266.302 159.273 266.657 146.715
240.7
125.91 116.776
2004VolvoS804D
2003FordCrownVictoria4D
253.25 257.028 267.539 242.661 246.546
0 237.736 243.518 110.464 142.235 235.282
98.935 242.393 125.311 130.758 127.109 239.087 103.044
2004VolvoS604D
137.265 121.892 253.587 126.634 253.395
90.182
95.894 181.019 115.939
138.17 241.458 114.632 122.939 169.054
83.914 257.363
2004VolvoS404D
257.127 249.718 112.063 247.175
2004FordTaurus4DSE
98.057
97.754
77.909
2005VolvoS804D
2004FordMustang2D
78.641 247.138
97.999 108.058
238.92 250.919 113.268 136.622 237.795 121.424 112.546
137.1
2005VolvoS604D
259.347
90.182
142.51
2005VolvoS404D
2004FordFoucs2DZX3
0
96.471 242.243
2000FordTaurus4DSE
139.138 116.893 244.634 123.531 248.392 125.626 136.456 155.768
94.552
100.9 253.797 264.963 260.258
192.82 136.715 252.106
126.55 126.034 120.282 241.636 101.793 100.442 174.657 108.293 245.627 119.134 129.055
127.31 105.788 112.225 260.509 102.645 245.835 129.347 114.536 102.844 245.557
145.03 240.877 122.998 130.711 104.552 259.367
2000FordMustang2DGT
94.085 243.888
2004FordFocus4Dwag
77.995 107.954
129.68
2000FordFocus4DLX
91.639 259.105
131.19 130.625 156.079
126.31 115.805 244.623
2000FordFocus4Dwag
112.99
137.749 124.156 249.346 124.501 254.607
240.7 252.309
200.62 254.776
134.36 266.377 138.053 144.701 209.924 159.707 259.623 146.157
2004FordFocus4DZX5
97.754
2000FordFocus2DZX3
97.999 242.661 252.077 112.391 128.507 240.455 132.698
0 118.076 107.954 108.058 246.546 252.733
2000FordEscort4DSedan
90.333 118.731 255.283 119.804 120.872
125.06 267.555 155.109 151.269 161.967 271.133 158.842 259.786 141.583 119.773
2000FordContour4D
93.225 256.361 145.553 134.826 137.747 268.762 129.403 251.303 140.199
240.4 132.677 119.868
123.25 127.681 242.134 127.827 143.909 151.257 183.724 245.094 122.758 123.437 251.446 125.609 108.818 131.146 122.848
78.302 255.575 254.452 246.147 213.063 256.933
138.6 190.509 133.968 249.333 125.365 155.231 146.427 247.309 132.305 130.321 130.337 244.379 131.685 147.359 156.218 190.196 247.424 127.199 130.664 249.261 137.202 115.675 131.953 136.571 197.758 134.602 129.588 135.124 135.967 144.006 131.602 129.777 143.532 137.607 143.188 133.489 149.439 139.069
89.901 253.409 108.027 256.423 265.434 257.911 102.878 256.645 260.929
120.04 132.461 253.735 140.311 256.916 137.676
94.552
120.67 127.405 135.917
2000FordCrownVictoria4D
77.995
252.98
99.249 146.715 252.671 128.868 129.207 130.211 256.844 148.216 254.113 103.991 145.884 137.572 253.806 133.528 129.964 197.209 142.713 252.308
147.145 134.025 251.215 140.143 256.665 148.755 137.725 162.751 110.743 118.076
0
145.92 252.749 138.034 105.877 113.581 259.965
84.33 253.573 257.362 193.622 249.754 115.616 253.084 265.211 257.541
2001FordTaurus4DSES
257.07 266.257 259.742
2001FordFocus4dwag
73.236 248.652 109.297
2001FordMustang2DGT
255.43 248.067
87.886 253.395 266.657 101.606 264.743 258.337
253.25 247.506
113.84 137.725 130.625 136.456 257.028 259.374 133.915
99.741 110.743
2001FordFocus4DLX
0
2001FordFocus4DSE
82.394 261.152
2004FordFocus4DZTW
99.741
2001FordFocus2DZX3
98.057
0 149.414 139.801 162.751 156.079 155.768 267.539 262.564 146.022
113.84 139.801
2001FordEscort4DSedan
267.02 140.889 121.901
2002FordTaurus4DSES
0 121.901 123.083
131.19 125.626
2001FordCrownVictoria4D
267.02 255.631 256.916 256.665 254.607 248.392
0 145.113 140.889 133.758 137.676 148.755
2002FordFocus4Dwag
0 251.392 263.617
2002FordMustang2DGT
78.641 112.063 253.587 266.302
0 246.951 121.102 152.782 150.697 125.111 140.311 140.143 124.501 123.531 247.138 247.175 126.634 159.273 247.388 145.601 138.073 126.115 250.562 134.839 249.126 131.378 154.208 146.017 248.359 136.145
93.01 246.951
2002FordFocus4DZX5
2002FordFocus4DSE
6 120.559 123.436 198.039
245.96 249.718 121.892 152.196 246.205 128.734 130.778 117.238 251.891 130.888 250.633 126.965 145.769 137.942 245.807 125.648 132.637 185.814 126.825 253.154 120.629 145.624 142.068 246.122 127.234
2004FordFocus4DZTS
245.96
2002FordFocus4DZTS
2002FordFocus2DZX3
2002FordEscort4DSedan
2003FordTaurus4dSES
129.717 131.365 251.936 125.111 255.631 133.758 123.083 149.414
2002FordCrownVictoria4D
2004FordFocus4DSVT
2003FordMustang2DGT
110.21 150.339 269.735 150.697
2003FordFocusZX3
2004FordCrownVictoriaSedan4D
2003FordFoucs4Dwag
98.507 147.246 263.499 152.782 263.617 145.113
2003FordFocus4DZX5
2004FordCrownVictoria4DLX
2003FordFocus4DSE
135.582 118.935 254.588 121.102 251.392
2003FordFocus4DZTS
262.195 247.829
2005FordTaurusSE4D
2003FordFocus2DZX3
144.88 120.288 248.132
2005FordMustangCoupe2DV6Deluxe
2003FordCrownVictoria4D
93.01 254.588 263.499 269.735 251.936 253.735 251.215 249.346 244.634
2005FordFocusZX54D
2004FordMustang2D
120.04 147.145 137.749 139.138 259.347 257.127 137.265 105.532 258.794 148.632 130.956 148.084 267.088 138.376 254.185 135.555 103.196 116.005
0 247.557 120.288 247.829 118.935 147.246 150.339 131.365 132.461 134.025 124.156 116.893
0 248.132
2004FordTaurus4DSE
2004FordFoucs2DZX3
2004FordFocus4Dwag
2004FordFocus4DZTS
2004FordFocus4DZX5
110.21 129.717
2004FordFocus4DZTW
98.507
2004FordFocus4DSVT
144.88 262.195 135.582
2004FordCrownVictoriaSedan4D
263.898 247.557
2005FordTaurusSE4D
141.692
2005FordFocusZX32D
2004FordCrownVictoria4DLX
2005FordFocusZX54D
141.692 263.898
2005FordFiveHundred4DSE
2005FordMustangCoupe2DV6Deluxe
2005FordFocusZX32D
2005FordCrownVictoria4D
0
2005FordFiveHundred4DSE
2005FordCrownVictoria4D
93.74
106.33
103.46
84.782 117.325 105.762
0 119.257 121.775 117.859 122.342
93.74 119.257
0 104.362 121.142
81.259
84.782 121.775 104.362
0
122.23 111.792
106.33 117.325 117.859 121.142
122.23
0 128.111
103.46 105.762 122.342
81.259 111.792 128.111
55
0
Preliminary Results
Unknown:2005FordFocusZX32D vs Only 2D Vehicles
120
100
80
Series1
60
40
20
0
2002FordMustang2DGT
2000FordMustang2DGT
2004FordMustang2D
2003FordMustang2DGT
2002ToyotaEcho2D
2000ToyotaEcho2D
2002ToyotaCelica2D
2001ToyotaCelica2D
2003ToyotaCelica2D
2000ToyotaCelica2D
2004ToyotaCelica2D
2005FordFocusZX32D
2001FordFocus2DZX3
2004FordFoucs2DZX3
2001ToyotaSolara2D
2003FordFocus2DZX3
2000FordFocus2DZX3
2000ToyotaSolara2D
2002FordFocus2DZX3
2003TotoyaSolara2D
2004ToyotaSolara2D
2005ToyotaSolara2D
2005ToyotaCelica2D
2005FordMustangCoupe2DV6Deluxe
56
Next Steps
Preliminary Conclusion:
Creating a feature vector composed of shape descriptors using shape contexts
demonstrates a high discriminatory power in vehicle identification.
ELATION!
57
Question #2 for the Class
• What were we to commit to memory as
part of class question #1?
• A novel application of existing methods
does not a dissertation topic make
58
Dissertation Timeline
Carl E. Abrams
Elation
Draft Chap 4&5
Now!
Draft Chap 3&4
Defense
1/06
1/05
1/04
9/03
Draft Idea Paper
Complete Draft
Proposal
Advisor Selection
Complete
Dissertation
First Paper at
Proposal
Pace Day
Final Manuscript
and Paper
Committee
Formation
Final Draft Chap
1-3
Draft Chap 1&2
59
Desperation!
• This is not new it is just an application of what is already
known!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
• Now what do I do?????
60
Dissertation Timeline
Carl E. Abrams
Desperation --- but never give in
Draft Chap 4&5
Now!
Draft Chap 3&4
Defense
1/06
1/05
1/04
9/03
Draft Idea Paper
Complete Draft
Proposal
Advisor Selection
Complete
Dissertation
First Paper at
Proposal
Pace Day
Final Manuscript
and Paper
Committee
Formation
Final Draft Chap
1-3
Draft Chap 1&2
61
Next Steps
Preliminary Conclusion:
Creating a feature vector composed of shape descriptors using shape
contexts demonstrates a high discriminatory power in vehicle
identification.
Research Exploration: How well can shape contexts perform in the
role of image segmentation???
1.Develop semantic extraction using Shape Contexts
2.Test segmentation / extraction method for semantic shapes
3.Compare segmentation / extraction method for semantic
shapes to other methods
62
Shape Extraction using Shape Contexts
1.
2.
3.
4.
5.
From known database develop invariant models for window shape 1,2 and
3, door shapes and body shape using clustering analysis
For these known shapes and their contexts, develop shape histograms for
neighborhood of high information context points on shape using entropy
measures
Scan across an edge detected image using a window, computing the local
neighborhood in the window
Compare the local neighborhood to the known shape contexts local
neighborhood and find best fits
Focus on best fits and match known shape to points in the best fit area
63
For known shapes and their contexts, develop shape histograms for neighborhood of high information
context points on shape using entropy measures
• Use entropy calculation to determine the local set of
points to use as reference on known shape context. (Note:
Shape Context is intentionally biased toward close in points)
• Use a subset of points on the known shape, slide around
the shape calculating entropy select the highest point
subset
(“The Entropy Strategy for Shape Recognition”, D Geman, Proc. 1994 IEEE-IMS
workshop on Information Theory and Statistics, Alexandria, VA, October, 1994 64
Shape Entropy Example
16 discrete samples from the curve
•12 flat line segments – 0 deg turning angle
•4 corners - 90 deg turning angle
Prob 0 deg =3/4 and Prob 90 deg = ¼
H ( x)   pi log pi  .8
i
Slide window
(“The Entropy Strategy for Shape Recognition”, D Geman, Proc. 1994 IEEE-IMS
workshop on Information Theory and Statistics, Alexandria, VA, October, 1994 65
Segmentation using Local Shape Contexts by looking for similar local shape histograms
Select the closet match and then fit the invariant model to the available points in the image by scaling
for best fit
66
Dissertation Timeline
Carl E. Abrams
Despair -- What cap and gown?
Draft Chap 4&5
Now!
Draft Chap 3&4
Defense
1/06
1/05
1/04
9/03
Draft Idea Paper
Complete Draft
Proposal
Advisor Selection
Complete
Dissertation
First Paper at
Proposal
Pace Day
Final Manuscript
and Paper
Committee
Formation
Final Draft Chap
1-3
Draft Chap 1&2
67
Beyond Desperation
Trying to regain the will to live
And then: …….
68
Introduction and Overview
• The research focuses on and extends the work done on a new
shape descriptor called “Shape Contexts”
• Constraining shapes to be continuous outlines (no holes) it is proved
using graph theory and then confirmed through experiments that the
original matching method which is modeled on the “Assignment
Problem” is subject to degenerate shape description
• A simpler matching algorithm called the “Least Cost Diagonal” which
utilizes the physical relationship of points on a boundary is
introduced and compared to the original method
• The efficacy of the Least Cost Diagonal method is confirmed
through experiments to the original method in a Nearest Neighbor
comparison using a previously classified pottery database
69
Shape Contexts-Assumptions
• Comparing two shapes was modeled after
“The Assignment Problem”
• Assumption: The boundary points are in
no particular order[2]
X
X
X
[2] T. B. Sebastian, P. N. Klein, and B. B. Kimia, "Recognition of shapes by editing their shock graphs," Pattern Analysis and Machine Intelligence,
IEEE Transactions on, vol. 26, pp. 550-571, 2004.
70
Shape Contexts-The Assignment
Problem
•
•
What is the best fit (minimum cost) to align all the points?
The Assignment Problem – Hungarian Method for bi-partite matching problem
We will be working with the following problem: assign n = 9 candidates to n=9 jobs to minimize the total salary cost
paid by the department. The individual salaries of each candidate at each job position depend on their qualification
and are given by the cost matrix (in $ per hour):
Sam
Jill
John
Liz
Ann
Lois
Pete
Alex
Herb
Administrator
20
15
10
10
17
23
25
5
15
Secretary to the Chair
10
10
12
15
9
7
8
7
8
Undergraduate Secretary
12
9
9
10
10
5
7
13
9
Graduate Secretary
13
14
10
15
15
5
8
20
10
Financial Clerk
12
13
10
15
14
5
9
20
10
Secretary
15
14
15
16
15
5
10
20
10
Web designer
7
9
12
12
7
6
7
15
12
Receptionist
5
6
8
8
5
4
5
10
7
Typist
5
6
8
8
5
4
5
10
7
If we start with the position of Administrator and assign it to Alex (he gets the minimal salary for this position), then we assign
the position of Secretary to Chair to Lois (he gets the minimal salary for this position), and so on, up to the position of Typist,
then the assignment is given by the assignment matrix:
71
Shape Contexts-Observations
• Using the assignment problem model
means that the neighborhood-ness of the
boundary points is not used.
• The potential of degenerate shape
description exists ( i.e. very different
shapes that use the same boundary points
but in which the points occurs in different
orders will be computed to be the same)
72
Shape Contexts-Degenerate
Behavior
X
X
3
3
X
X
4
2
X
5
X
X
7
6
X
X
X
X
X
1
X
5
4
2
3
X
X
X
7
6
X
X
X
7
6
4
2
X
1
X
1
Point
X
Y
Point
X
Y
Point
X
Y
1
0.00
0.00
1
0.00
0.00
1
0.00
0.00
2
-.7071
.7071
2
-.7071
.7071
2
-.7071
.7071
3
0.00
1.4142
3
0.00
1.4142
3
.866
.5
4
.866
.9142
4
.866
.5
4
0
1.4142
5
1.866
.9142
5
.866
.9142
5
.866
.9142
6
1.866
.500
6
1.866
.9142
6
1.866
.5
7
.866
.500
7
1.866
.500
7
.866
.500
Shape 1
5
Shape 2
Shape 3
Distance from Shape 1 – Hungarian 0
Distance from Shape 1 – Hungarian 0
Distance from Shape 1 – LCD 25
rotation 0 degrees
Distance from Shape 1 – LCD 26
rotation 50 degrees
73
Shape Contexts-Degenerate
Behavior Formalization
Graph Theoretic Proof with Examples
Jordan Curve Theorem
Step 1: Using the definition of a Hamiltonian Cycle prove using
If J Jordan's
is a simpleTheorem
closed curve
in each
R2, then
the Jordan
curve theorem,
that
different
Hamiltonian
Cyclealso
of acalled
set
the Jordan-Brouwer
1966) states that
J Hamiltonian
-has two
of vertices is atheorem
distinct (Spanier
shape corresponding
toR2
that
components
Cycle (an "inside" and "outside"), with J the boundary of each.
The Jordan curve theorem is a standard result in algebraic topology with a rich
history. A complete proof can be found in Hatcher (2002, p. 169), or in classic
that
the Hamiltonian
Cycles
of awas
particular
textsStep
such2:
asProve
Spanier
(1966).
Recently, a proof
checker
used bygraph
a
which have team
beentoshown
represent distinct shapes
aretheorem
Japanese-Polish
create to
a "computer-checked"
proof of the
(Grabowski
2005).
computed
to be identical by the shape context method developed
by Belongie (solving point assignment matching using the
http://mathworld.wolfram.com/JordanCurveTheorem.html
Assignment Problem as the model)
74
Shape Contexts-Proof of
Degenerate Behavior
Step 1
Let a shape S on a binary image I be a connected component where every pixel whose
value is 1 is an element of S. A shape S partitions the two dimensional plane into two
regions: the interior (union of pixels whose value is 1) and exterior (union of pixels
whose value is 0).
Lemma 1: Every Hi for a unit graph G forms a certain shape Si .
Proof: Hi is a closed cycle graph by definition or an n-gon if G is planar. By the Jordan
curve theorem [12,[4]15], Hi partitions the plane into two regions, the interior and exterior.
Painting the interior and exterior regions with black and white colors, respectively,
produces a connected component shape. Thus, every H i for a unit graph G forms a
certain shape Si .
[4] O. Veblen, "Theory on plane curves in non-metrical analysis situs," Transactions of the American Mathematical Society vol. 6, pp. 83-98, 1905.
75
Shape Contexts-Proof of
Degenerate Behavior
Step 1 Example
G
H1
H2
H3
H4
H5
H6
H7
76
Shape Contexts-Proof of
Degenerate Behavior
Step 2
Theorem 1: All shapes Si formed by Hi for a unit planar graph G are considered
identical shapes by SC algorithm if p1  V .
Proof: Let S and S’ be two distinctive shapes formed by two Hamiltonian cycles for a
unit graph G where V  {v1 vn} . The SC algorithm will place pi s exactly on vertices on
G since all edges are unit distance d in length. Let h and h’ be polar histograms for S and
S’, respectively. Since locations of vertices are the same in two shapes, h = h’. Hence, we
can derive the following from the eqn (2).
b
Ci ,i  1/ 2
 [h(k )  h '(k )]
2
/( h( k )  h '( k ))  0 where i  v1
vn
k 1
SCdistance ( S , S ') 
vn
C
i ,i
0
i  v1
Therefore, all shapes Si formed by Hi for a unit planar graph G are considered identical
shapes by the SC algorithm if p1  V .
?
77
Shape Contexts-Proof of
Degenerate Behavior
Step 2 Example
S1
S2
S3
S1
S1
(a)
S1
S2
S3
S1 S2
S3
0 105 0
105
0 106
0 106
0
(b)
78
The Least Cost Diagonal
• Constrains the domain of shapes to
outlines (no holes)
• Constrains matches to made up of
boundary ordered points
• Avoids degenerate shape matching
• Runs much more quickly than the original
method
• Gives an indication of degree of rotation of
one shape to another
79
The Least Cost Diagonal-How it
works
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
Point
Point
2
3
4
5
6
1
0.00
6.00
10.00
10.00
10.00
4.00
1
2
6.00
0.00
6.00
6.00
10.00
6.00
2
1
2
3
4
5
6
6.00
4.00
0.00
8.00
3.33
10.00
8.00
6.00
8.00
0.00
6.00
10.00
6.00
10.00
10.00
8.00
6.00
3.33
6.00
0.00
10.00
6.00
6.00
0.00
6.00
6.00
4
6.00
10.00
10.00
10.00
10.00
0.00
5 10.00
10.00
10.00
6.00
0.00
6.00
5
0.00
8.00
6.00
8.00
8.00
6.00
6
6.00
10.00
6.00
6.00
0.00
6
8.00
0.00
4.00
6.00
6.00
10.00
0 degrees
90 degrees
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
4.00
3
x
6.00
Point
Point
0.00
4 10.00
3 10.00
x
1
x
x
x
x
x
x
x
x
x
x
x
Point
Point
1
1
2
3
4
5
6
6.00
4.00
0.00
8.00
3.33
10.00
6.00
8.00
0.00
8.00
6.00
10.00
3
8.00
6.00
3.33
6.00
0.00
10.00
4
6.00
10.00
10.00
10.00
10.00
0.00
Point
Point
2
1
2
3
4
5
1
3.33
10.00
6.00
4.00
0.00
8.00
2
6.00
10.00
8.00
6.00
8.00
0.00
3
0.00
10.00
8.00
6.00
3.33
4 10.00
0.00
6.00
10.00
10.00
8.00
6.00
0.00
8.00
6.00
8.00
6.00
10.00
8.00
0.00
4.00
6.00
5
0.00
8.00
6.00
8.00
8.00
6.00
5
6
8.00
0.00
4.00
6.00
6.00
10.00
6
270 degrees
degrees
180
6
180
degrees
270
degrees
6.00
10.00
80
Shape Contexts-Degenerate
Behavior
X
X
3
3
X
X
4
2
X
5
X
X
7
6
X
X
X
X
X
1
X
5
4
2
3
X
X
X
7
6
X
X
X
7
6
4
2
X
1
X
1
Point
X
Y
Point
X
Y
Point
X
Y
1
0.00
0.00
1
0.00
0.00
1
0.00
0.00
2
-.7071
.7071
2
-.7071
.7071
2
-.7071
.7071
3
0.00
1.4142
3
0.00
1.4142
3
.866
.5
4
.866
.9142
4
.866
.5
4
0
1.4142
5
1.866
.9142
5
.866
.9142
5
.866
.9142
6
1.866
.500
6
1.866
.9142
6
1.866
.5
7
.866
.500
7
1.866
.500
7
.866
.500
Shape 1
5
Shape 2
Shape 3
Distance from Shape 1 – Hungarian 0
Distance from Shape 1 – Hungarian 0
Distance from Shape 1 – LCD 25
rotation 0 degrees
Distance from Shape 1 – LCD 26
rotation 50 degrees
81
Experimental Results
• Basic Shape Context Experiments
• Classification Performance
• Timing and Boundary Point dependence
82
Experimental Results
• Basic Shape Context Experiments
– Translation, Scale and Rotation Invariance
– Shape Matching with the Least Cost Diagonal
– Demonstration of Degenerate Shape
Description/Matching
83
Experimental Results
• Classification Performance
– Use a NN analysis on a known database
which had already been grouped into
classes[5]. Compare the Assignment Model
Matching to the Least Cost Diagonal at
varying number of boundary points
– Experiments performed:
• One pottery class against each other class
• One pottery class against groups of two classes
• One pottery class against groups of three classes
What happened to the
cars?????
[5] RG. Bishop, S. Cha, and C.C. Tappert, "Identification of Pottery Shapes and Schools Using Image Retrieval Techniques," Proc. MCSCE,
CISST, Las Vegas, NV, June 2005.
84
Experimental Results
• Timing and Boundary Point dependence
– Timing: From all the classification runs plus
additional runs at higher number of boundary
points verify the order of computational
complexity
– Point dependence: Run the classification on
particular classes that did not perform well at
additional boundary points(150 and 250) to
demonstrate that a relationship between the
number of points and performance exists
85
Classification Experimental Results
• Experimental Procedure: Compute % correctly
classified for various numbers of boundary points
(3,4,5,10,20,100)[6,7]
Class 1 Shapes
SA1
SA2
SA3
SA4
SA5
Class 2 Shapes
SA6
SA7
SB1
SB2
SB3
SB4
SB5
Class 1 Shapes
SA1
SA2
SA3
SA4
SA5
SA6
SA7
[6] S. Belongie, J. Malik, and J. Puzicha, "Matching Shapes," presented at International Conference on Computer Vision (ICCV'01) Volume 1 2001.
[7] K. Mikolajczyk and C. Schmid, "A performance evaluation of local descriptors," Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 27, pp.
1615-1630, 2005.
86
Classification Experimental Results
100
100
90
90
80
80
70
70
60
Diagonal
50
Hungarian
40
% Correct
% Correct
Example: Class 1 vs Class 2 and Class 3
60
Hungarian
40
30
30
20
20
10
10
0
0
100points
20points
10points
5points
4points
3points
0points
100points
20points
10points
5points
4points
3points
0points
Number of Points
Diagonal
50
Number of Points
87
Classification Experimental
Results: Timing
• Averaged the timing for each set of boundary points-60
runs per boundary point. (Added six additional runs to
extend the timings up to 250 points)
• Fitted a polynomial to the data to derive the
computational order of complexity
Least Cost Diagonal Timing
Hungarian Timing
0.02
Time in Seconds
Time in Seconds
250
200
150
100
50
0
-50 0
0.015
0.01
0.005
0
100
200
300
400
Number of Points
500
600
0
100
200
300
400
500
600
Number of Points
88
Classification Experimental
Results: Timing Curve Fitting
89
Classification Experimental
Results: Boundary Points
Class 1
Class 5
90
Classification Experimental
Results: Boundary Points
Class 1 vs Class 5 250 boundary
points
100
100
90
90
80
80
70
70
60
Diagonal
50
Hungarian
40
% Correct
% Correct
Class 1 vs Class 5
60
250points
150points
100points
20points
10points
5points
4points
3points
0points
100points
20points
10points
5points
0
4points
10
0
3points
20
10
0points
30
20
a
Hungarian
40
30
Number of Points
Diagonal
50
Number of Points
b
91
Classification Experimental
Results: Boundary Points
Class 3 vs Class 11,12,13 250 points
100
100
90
90
80
80
70
70
60
Diagonal
50
Hungarian
40
% Correct
% Correct
Class 3 vs Class 11,12,13
60
Hungarian
40
30
30
20
20
10
10
0
0
250points
150points
100points
20points
10points
5points
4points
3points
0points
100points
20points
10points
5points
4points
3points
0points
Number of Points
Diagonal
50
Number of Points
92
Summary
•The research has confirmed the properties of the Shape Context and
proved that when the domain of the shape matching is limited to
outlines, erroneous shape matching based on degenerate shape
descriptors can occur
•A new matching method has been introduced( Least Cost Diagonal)
and its efficacy verified against real images and compared to the
original method developed by Belongie
•Further work was identified:
•Is there a relationship between the number boundary points to the
accuracy of shape matching?
•Are there better distance measures for shape context
histograms?
•Are there better quantizing schemes for shape contexts?
93
Dissertation Timeline
Carl E. Abrams
Defense was on
July 14th, 2006
Draft Chap 4&5
Now!
Draft Chap 3&4
Defense
1/06
1/05
1/04
9/03
Draft Idea Paper
Complete Draft
Proposal
Advisor Selection
Complete
Dissertation
First Paper at
Proposal
Pace Day
Final Manuscript
and Paper
Committee
Formation
Final Draft Chap
1-3
Draft Chap 1&2
94
Post-Defense Work
• Apply the new method to my existing car
database
– Compare to “Hungarian Method” for accuracy
and speed
• Make the dissertation longer
95
Discussion/Lessons Learned
•
•
•
•
•
•
•
Really have an interest in your topic!!
Get comfortable with uncertainty
See your faculty advisors early and often
Work on your project ever day!!!
Don’t throw anything away
Make it at least 100 pages long without the references
“The way to get good ideas is to get lots of ideas, and
throw the bad ones away”
– Dr. Linus Pauling
(The trick is recognizing the bad ones)!
Dr. Carl Abrams – DPS Class of 2006)
96
An Overview of IBM Research
IBM Research
Global Technology Outlook 2013 - Do Not Distribute
© 2013 IBM Corporation
GTO 2013 Introduction
IBM Research Overview
60 Years of Innovation
1970s
1980s
 Corporate
funded
research
agenda
 Technology
transfer
1990s
 Collaborative
team
 Shared agenda
 Work on
client
problems
 Effectiveness
2000s
 Create business
advantage for clients
 Industry-focused
research
Research Partnership
Innovation Labs
Research Institutes
Collaborative Innovation
Research in the Marketplace
Joint Programs
Research Services
Centrally Funded
Collaborate on client-specific technology and business solutions
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FOAK
First of a Kind
98
GTO 2013 Introduction
Diversity of Disciplines at IBM Research
Behavioral
Sciences
Materials
Science
Chemistry
Mathematical
Sciences
Computer
Science
Physics
Electrical
Engineering
Service Science,
Management &
Engineering
Science &
Engineering
Technology
Innovation
Business
Innovation
Social
Innovation
Demand
Innovation
Social & Cognitive
Sciences
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Business &
Management
Economics &
Markets
99
GTO 2013 Introduction
IBM scientists first to distinguish individual molecular bonds
 Zurich, Switzerland, 14 September 2012—IBM (NYSE: IBM) scientists have been able to
differentiate the chemical bonds in individual molecules for the first time using a technique
known as non-contact atomic force microscopy (AFM).
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GTO 2013 Introduction
President Obama Awards IBM Scientists with National Medal of Technology
and Innovation for Inventing the Underlying Technology in LASIK Surgery
 WASHINGTON, D.C. - 01 Feb 2013: President Obama will honor a team of three IBM
(NYSE: IBM) scientists -- James J. Wynne, Rangaswamy Srinivasan and Samuel Blum -with the National Medal of Technology and Innovation, the country's most prestigious award
given to leading innovators for technological achievement. They are receiving this award for
their discovery of a new form of laser surgery, using an excimer laser that made modern
LASIK and PRK refractive eye surgery possible.
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GTO 2013 Introduction
Focus Areas at IBM Research
Industry Solutions
Cybersecurity
Cloud
Business Analytics
Future Systems
Global Technology Outlook 2013 - Do Not Distribute
Processors /
Storage / Switching
© 2013 IBM Corporation
Nanotechnolgoy
102
GTO 2013 Introduction
Financial Services Industry Solutions Research
Banking, Financial Markets, Insurance Platforms
Systemic Risk & Regulation
LDAR
MRAN
Industry @ Scale
Compliance Accelerator
Content Ingestion
Liquidity Risk
LRX
Collateral,
Settlement Risk
Transactions
@Scale
Fraud
Surveillance
Operational & IT Risk
quantification & predictive analytics
Cyber Security
Scalable and Resilient
Business Services
Complex adaptive systems
Client Services
Client Lifetime Value
Mobile and eCurrency
Smarter Commerce
‘Edge’ Analytics
Marketing Optimization –Next Best Action
SMARC/Social Business
Analytics/Simulation/Big Data/Visualization
Technology Platforms/Cognitive Computing/Cloud
Institutes and Co-laboratory
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GTO 2013 Introduction
FOAK Projects
Banking
Financial Markets
Insurance
2012
– iPro( Intraday Cash Settlement Predictive
Analytics)
– Edge Analytics( Real time customer analytics )
2011
– SMARC Social Media Analytics for Retail
Customers
2010
– Advanced Digital Wallet for Financial Services
– Social Empathy Selling for Banking
2009
– Systemic Risk Facility
– Risk Adjusted Performance Management
2008
– Information Risk and Compliance Monitor
– Core Banking Transformation
2007
– VofC (Real time speech analytics in the call
center)
– Touch-less Secure Desktop
– Business of IT Dashboard
2006
– Predictive Application Life Cycle Management
2005
– Operational Risk Modeling and Quantification
– SCORE: Symbiotic Context Oriented
Information Retrieval
2004
– On Demand Banking transformation Solution
– Smart Customer Interaction
2003
– Conversational Biometrics
2012
– MRAN (Multi level Risk Analytic Network)
2011
– Mark to Liquidity (LiRA)
2010
– Loan Data Analytics for Risk (LDAR)
2009
– Systemic Risk Facility
2007
– High Performance Stream Processing for
Financial Services
– Integrated Power, Cooling and IT Management
for Data Centers
2006
– Mirage: Data Center Storage Optimization
2005
– Fossilization of Business Records
2003
– Monte Carlo Grid for Risk Management
2002
– Grid Architecture for Financial Services
1998
– Voice Recognition enabled Customer
Relationship Intelligence
2012
– LAQORM: Linking & Analysis of Quantifiable
Operations Risk Metrics
2009
– Operation Integrity and Compliance
Management
2008
– Continuous Compliance
2007
– Optimizing Business Rules Creation and
Validation in Claims Processing
2006
– Information Integration of Distributed
Warehouse Information
2005
– Predictive Model Management Middleware
– Rule-based Claims Processing
2004
– Insurance Application Transformation Based on
Business Rule Extraction and Clustering
2003
– On Demand Business Process integration for
Financial Services: Core Insurance
1999
– XML Based Insurance
1998
– Virtual Market Place
– Mobile Insurance Worker
– Data Hiding for Automobile Insurance Claim
Process
Source: http://w3.research.ibm.com/FOAKS/foak1.html
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GTO 2013 Introduction
Computation Power , Storage, Bandwidth
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105
IBM RESEARCH
Jeopardy - The IBM Challenge
► 1997 - Chess
■ A finite, mathematically well-defined search space
■ Large but limited number of moves and states
■ Everything explicit, unambiguous mathematical rules
► 2011 - Human Language
■ Ambiguous, contextual and implicit
■ Grounded only in human cognition
■ Seemingly infinite number of ways to express the
same meaning
[106]
2011-02-23
IBM RESEARCH
DeepQA: Massively Parallel Probabilistic
Evidence-Based Architecture
Question
100s sources
100s Possible
Answers
1000’s of
Pieces of Evidence
100,000’s scores from many
simultaneous Text Analysis Algorithms
Multiple
Interpretations
Question &
Topic
Analysis
Question
Decomposition
Hypothesis
Generation
Hypothesis
Generation
Hypothesis and Evidence
Scoring
...
[107]
Hypothesis and
Evidence Scoring
Synthesis
Final Confidence
Merging &
Ranking
Answer &
Confidence
2011-02-23
IBM RESEARCH
Art of the Possible: Transforming Healthcare with
IBM Watson
► "IBM's work with WellPoint and Memorial Sloan-Kettering Cancer Center represents a
landmark collaboration in how technology and evidence-based medicine can transform the
way in which healthcare is practiced. These breakthrough capabilities bring forward the first
in a series of Watson-based technologies, which exemplifies the value of applying big data
and analytics and cognitive computing to tackle the industries most pressing challenges."
►
Manoj Saxena, IBM General Manager,
Watson Solutions
[108]
2011-02-23
IBM RESEARCH
The fourth dimension of Big Data: Veracity – handling data in doubt
Volume
Velocity
Variety
Veracity*
Data at Rest
Data in Motion
Data in Many
Forms
Data in Doubt
Terabytes to exabytes
of existing data to
process
Streaming data,
milliseconds to
seconds to respond
Structured,
unstructured, text,
multimedia
* Truthfulness, accuracy or precision, correctness
[109]
Uncertainty due to
data inconsistency
& incompleteness,
ambiguities, latency,
deception, model
approximations
IBM RESEARCH
The Challenge of Big Data and its Implications
► Extremely Large Volumes
► Platforms to Process
► Disparate Sources and Forms
► Analyze both Structured and
Unstructured Data
► Near Real-Time
► Ingest in Real-Time
► Short Lived Value
► Analytics in Real-Time
► Inherently Ambiguous
► New forms of Analytics
[110]
S&D/Research
SMARC Solution Architecture
Public Data
Data Ingestion
Adapters
Twitter
Online social media
analytic libraries
Promotion
Scoring
Output Data
Blogs/Boards
SystemT, Midas, SystemML
MySpace
Google Buzz
Online promo
stream
InfoSphere Streams
Facebook
Protocols for data
& control exchange
Customer
Models
Private Data
Transaction
Data
Offline social media
analytic libraries
Consumer
List
Promotion
models
SystemT, Midas, SystemML
Product
Categories
InfoSphere BigInsights
Public consumer
profiles mapped
to internal
Customer ID or
Identified as
prospects
Promotion
Information
Client Data
111
2011 FOAK Program
Existing analytics assets leveraged
IBM Confidential
FOAK Assets to be developed
© 2011 IBM Corporation
Email from
customer
Customer
Insights
Web store
Social
media
data
capture
Comments by
customer on
Facebook, Twitter
Customer interact via
multiple channels
112
[112]
Customers purchase
history and other past
interactions across
channels use to infer
general insights about
the customer such as
lifetime value and
tastes
Next Best
Action
Decisioning
Module
Contact
Center
Agent
Customer’s
smart phone
Customer
Real-Time
State
Store staff’s
smartphone
From
Customer’s
Smartphone
Email to
customer
Contact center
agent inputs
Business rules
operating on
extracted customer
insights and
customer state
generate NBA
recommendations
Service
Staff
Channel Backend Systems
POS data
Composite view of
recent interactions
between customer and
business captured as
“customer state” –
such as - immediate
goal, sentiment,
urgency.
Deliver Action to
channel
Channel Backend Systems
Multi-channel Next Best Action (MNBA): Business Problem
Instructions
to respond
on facebook
IBM RESEARCH
Examples of Actions : Replace
broken item (rather than
repair), OR offer certain
discount on upgrade
2011-02-23
IBM Business Analytics and Optimization
MNBA realizes state-based marketing via NBA Optimizer
 Can be viewed as a path through the stages of shopping
 Customer is in some state (his/her attributes) at any point in time
 Enterprise's action will move customer into another state
 Enterprise's goal is to take sequence of actions to guide customer's path to maximize customer's
lifetime value
 Reinforcement Learning produces optimized targeting rules of the form
 If customer is in state "s", then take marketing action "a"
 Customer state “s” represented by current customer attribute vector
Campaign E
Customer A’s path under…
Current marketing policy
Optimized marketing policy
Potentially
Valuable
Loyal
Customer
Loyal
Customer
Repeater
Repeater
Repeater
One Timer
Defector
Defector
Campaign C
Campaign A
Bargain
Hunter
Campaign B
© 2012 IBM Corporation
Valuable
Customer
Campaign D
113
IBM RESEARCH
Temporal Causal Modeling for Operational Risk Quantification
Aggregate Analysis
(Process & Business
Entity Level)
Induced Loss Distribution
Advanced Scenario Generation:
Probabilistic Graphical Model
Structure, Parameters; Elicited
from Experts
Optimal Mitigation
Portfolio Analysis
Causal (KRI -> Loss
Event) Analysis
Capture Risk Mitigation Costs
with residuals
Common Data Services API
Data Sources
[114]
2011-02-23
IBM RESEARCH
IBM Unveils Cognitive Computing Chips…
ARMONK, N.Y., - 18 Aug 2011: Today, IBM (NYSE: IBM) researchers unveiled a new
generation of experimental computer chips. IBM’s first neurosynaptic computing chips
recreate the phenomena between spiking neurons and synapses in biological systems, such
as the brain, through advanced algorithms and silicon circuitry. Its first two prototype chips
have already been fabricated and are currently undergoing testing.
Called cognitive computers, systems built with these chips won’t be programmed the same
way traditional computers are today. Rather, cognitive computers are expected to learn
through experiences, find correlations, create hypotheses, and remember – and learn from –
the outcomes, mimicking the brains structural and synaptic plasticity.
Each core contains 256 clusters of transistors and
thousands of random access memory (RAM) elements
Computational elements and RAM are wired together
RAM emulates neuron, computation emulates synapse
[115]
2011-02-23