By
Sridhar Godavarthy
Co-Author:
Joshua Candamo Ph.D
Advisors:
Dr. Kasturi Rangachar
Dr. Dmitry Goldgof
 Motivation
 Problem Definition
 Previous Work
 Baseline
 The Algorithm
 The Profile Model
 The performance metric
 Results 
 Samples and Other Applications
- “A picture is worth a thousand words"
The United States army reports that they have lost more helicopters to power lines than
against enemies in combat [*]
[*] P. Avizonis and B. Barron, “Low cost wire detection system” Digital Avionics Systems Conference, vol. 1, pp. 3.C.3-1-3.C.3-4, 1999.
 In image processing applications, objects are typically represented
without accounting for information from their surroundings. A
novel approach to represent the profile of objects using Gaussian
models is presented. The profile is a representation of the object
and its surrounding regions. The profile model is empirically
shown to be effective and easily applicable to several object
detection tasks.
 Synthetically generated images [*]

 High altitude

Videos with real wires
Low altitude urban scenes
[*] R. Kasturi, O. Camps, Y. Huang, A. Narasimhamurthy, and N. Pande, “Wire Detection Algorithms for Navigation,” NASA
Technical Report, 2002.
Previous Algorithm Example
Image
Boundary-based
Feature Map
Post-Processing
Pattern
Matching
Detected
Objects
1
Hough
max( votes)  min( votes)
Threshold  mean(votes
)  Transform
2
 Support Vector Machine (SVM):
 Found to be not suitable for thin wires
 Difficult to provide a good set of positive and negative
examples
 “Future research should explore
 1) integration over time of the obtained results to detect
very thin wires and
 2) use image context”
Gandhi, T., Yang, M.T., Kasturi, R., Camps, O., Coraor, L., McCandless, J. “Performance Characterization of the Dynamic Programming Obstacle Detection
Algorithm”, IEEE Trans. on Image Processing, vol. 15, no. 5, pp. 1202-1214, 2006.
Gandhi, T., Yang M. T., Kasturi, R., Camps, O., Coraor, L., McCandless, J., “Detection of obstacles in the flight path of an aircraft” IEEE Trans. Aerospace and
Electronic Systems, vol. 39, no. 1, pp. 176–191, 2003.
100
Detection (%)
90
80
70
60
50
Algorithm Described In [*]
40
30
20
Baseline [**]
Algorithm
Described In [**]
(Baseline)
10
0
0
5
10
15
20
25
False Alarm (%)
[*] J. Candamo and D. Goldgof, "Wire Detection in Low-Altitude, Urban, and Low-Quality Video Frames," International Conference Pattern
Recognition, pp. 1-4, 2008.
[**] J. Candamo, R. Kasturi, D. Goldgof, and S. Sarkar, "Detection of thin lines using low quality video from low altitude aircraft in urban
settings," IEEE Transactions on Aerospace and Electronic Systems, vol. 45, no. 2, 2009.
Video
Frames
Edge
Detection
Noise
Reduction
Line
Fitting
Support Pixels
Profile
Analysis
Final
Wires
Scene
Correction
Weight
Thresholding
Initial Wire
Estimates
Video
Frames
Edge
Detection
Noise
Reduction
Line
Fitting
Support Pixels
Profile
Analysis
Final
Wires
Scene
Correction
Weight
Thresholding
Initial Wire
Estimates
 Each connected component in the feature map is
represented using a chain code.
 Compute the chain code histogram:
 Only the pixels labeled with the code with the highest
count in the histogram are kept.
nv
hist (v)  , v  0,...,7
n
0
0
0
1
1
7
1
7
1
Video
Frames
Edge
Detection
Noise
Reduction
Line
Fitting
Support Pixels
Profile
Analysis
Final
Wires
Scene
Correction
Weight
Thresholding
Initial Wire
Estimates
 Straight lines:
y  mx  c
 Fitting is done through regression, minimizing the
squared error:
2
(
y

(
mx

c
))
 i
i
i
 A 4% fit improvement, leads to 15% ROC performance
detection increase
Video
Frames
Edge
Detection
Noise
Reduction
Line
Fitting
Support Pixels
Profile
Analysis
Final
Wires
Scene
Correction
Weight
Thresholding
Initial Wire
Estimates
Thin Objects
Wire
p( z ) 
1
(2 )d / 2 
1/ 2
 1

exp  ( z   )T 1 ( z   )
 2

Surrounding
Regions
Object
Profile
hist(l)
G3
G2
Looking for Symmetrical Profiles
G1
l
0
47
96
178
255
Video
Frames
Edge
Detection
Noise
Reduction
Line
Fitting
Support Pixels
Profile
Analysis
Final
Wires
Scene
Correction
Weight
Thresholding
Initial Wire
Estimates
 Let the set of all wire candidates that “survived”
the weight thresholding be S.
| median{ms }  mi | tan( )
where mi is the slope of the wire candidate, mS is
the set of slopes corresponding to S, and θ is the
angle deviation required for a wire to be
considered correctly detected.
Wire Detection Example
Image
Boundary-based
Feature Map
Pre-Processing
Post-Processing
Pattern
Matching
Initial Detected
Objects
Detected
Objects
Profile
Estimation
 within an angle of 100 and y-intercept within 20 pixels
of the ground truth
 Reasonable.
 Based on psychological studies of human perception
100
90
Detection (%)
80
70
60
50
Proposed
Algorithm
Final
Final
Algorithm
Algorithm
40
Preliminary
Preliminary
Algorithm
Algorithm
[*]In
[*][*]
Algorithm
Described
30
20
Algorithm
In [**]
Baseline
Baseline
[**]Described
[**]
(Baseline)
10
0
0
5
10
15
20
25
False Alarm (% )
[*] J. Candamo and D. Goldgof, "Wire Detection in Low-Altitude, Urban, and Low-Quality Video Frames," International Conference Pattern
Recognition, pp. 1-4, 2008.
[**] J. Candamo, R. Kasturi, D. Goldgof, and S. Sarkar, "Detection of thin lines using low quality video from low altitude aircraft in urban
settings," IEEE Transactions on Aerospace and Electronic Systems, vol. 45, no. 2, 2009.
 No Tracking
 Low quality video
 Handles cluttered background
 Specifically designed for low altitude flight
 Robust
 Visual Complexity
 Choppy Wires
Questions?
Merci beaucoup pour votre patience
Sridhar Godavarthy
sgodavar @ cse.usf.edu
www.cse.usf.edu/~sgodavar
 Found Videos are videos downloaded from the
Internet
 UAV: Unmanned Aerial Vehicles
Total
Training
592 pixel blobs from
detection 
correctly detected pixels
total street ground truth pixels
16 traffic images
Found Videos
40 videos
UAV Videos
15 minutes of video
false alarm 
false alarm pixels
total image pixels
Line i=(ρi, θi)
Definition not concise
2 lines are equal if:

  40

Line j=(ρj, θj)
  40 px


Weak feature map led to high FA rate
HT is a weak pattern recognition method in
reality: trigonometric operations are slow, and
only robust to noise if you have low clutter
 Within the application domain:
 2 image wires i & j are equivalent if they can be used
interchangeably to describe the same true wire
 i & j are similar if they are likely to be perceived as equal by
a human operator
 We define equivalency of wires as
i  j  m1  m2  PAR _ SIM _ m  c1  c2  PAR _ SIM _ c
 And line similarity as
i  j  m1  m2  2PAR _ SIM _ m  c1  c2  2PAR _ SIM _ c
PAR_SIM_m = 0.2 (about 10o)
PAR_SIM_c = 20px
Image
Support Points
Canny
Connected Component Labeling
Low Level Image
Understanding
Strong Feature Map
Morphological Filtering
(No Training)
Line Fitting
Lines
Lines
Combine Similar Lines
Steger’s Line Profile
Global Line Direction Similarity
Threshold
Wires
High Level Image
Understanding
(Training)
Kasturi’s Threshold:
wmax  wmin
T  base 
2
Base= control variable for ROC
a  1 t  0 sign  1 comp(1)  1 comp(1)  1
Primitive mean
Compute distance d to ith Gaussian
i  comp(sign )
No
comp( sign )  a
sign  0
sign  1
No
sign  1
Use pixels along
Initialize ath Gaussian
y  mx  c  t (sign )
Update ith Gaussian
a  a 1
No, i.e. 1st iteration
a 1
No
If
d  Td
sign  0
Then
t  t 1
a3
Complete iterations for G3
p j  z | x ,
j  1..3
Original Edge Map
Feature Map
y  mi x  ci  t
weightmax
Wire Candidate i
Using t = -1 0 1
Support Pixels
Initial Weight (pixels)
Weight Thresholding
Sum
No
Are all Wire Candidates Done?
Next candidate
Yes
Scene Correction
 “Most wire pixels are edge pixels”
 “Most of edge pixels are not wire pixels”
 Domain Definitions/Assumptions:

m
 A wire i is described by the 3-tuple
( wi , mi , ci )
weight, slope, and y-intercept
y  mx  c
 The weight is the # of pixels conforming the wire
 PAR_SIM_m = 0.2 (about 10o)
 PAR_SIM_c = 20px