Part A-1: Run the following templates and determine their tasks.
In this problem set, you will apply different template operations and determine proper initial settings.
Use the following notations from the textbook to specify initial network settings. If a static binary
image P is given then initial conditions should be specified as
Input: U(t) = P / arbitrary / value = [-1;1];
e.g. U (t) = P
Initial state: X(0) = 0 / P / arbitrary / value = [-1; 1]
e.g. X (0) = arbitrary
Boundary condition: fixed value = [-1; 1] / zero-flux / periodic e.g. zeroflux
All templates and images can be obtained from the directory “\Temlib” and “\Piclib” respectively from
CANDY.
Run the following templates on the simulator and paste your outputs on the attached blank solution
forms. Specify proper initial settings (this is the trickiest part! – try several options). What task is
performed by each template?
Number
Template
1.
EDGE
Image (P)
Output
CONC2.BMP
Network Settings
Input - U: Picture Initial State - X(0): Arbitrary
Boundary: Zero-flux, Gray, Periodic
Task: Candy running 1 tau, with 0,1 step. If I change tau smaller then 1 (0,5), then I get This:
The reason is that, it can’t running completely.
1
Number
Template
2.
DIFFUS
Image (P)
Output
MADONNA.BMP
Network Settings (select a specific running time)
Input - U: Gray background
Initial State - X(0): Picture
Boundary: Fixed 0
Tau=1
Task: This template made a smoother picture.
The result of Tau=5:
Number
Template
3.
EROSION
Image (P)
Output
FIGREC.BMP
Network Settings
Input - U: Picture Initial State - X(0): Arbitrary
Boundary: Fixed 0
Tau=10
2
Here the erosion is depend from tau, such as Tau=2, we can find gray squares:
When the state value=5, then
Number
Template
4.
SHADOW
Image (P)
Output
HOLLOW.BMP
Network Settings (set a proper running time!) - Running time: 150
Input - U: Picture
Initial State - X(0): Value: 1
Boundary: 0
When the Initial State is arbitrary, then nothing happen. Otherwise a shadow to left.
Number
Template
Image (P)
Output
3
5.
GRADIENT
AVERGRA1.BMP
Network Settings
Input - U: Picture
Initial State - X(0): Arbitrary
Boundary: Zero-flux
It’s a simple gradient search.
4
Part A-2: Design simple zero-input templates
Design 2 zero-input (autonomous) class CNN templates - C(A, 0, z) - to produce the following 2
outputs from the image on the left (AVERGRA2.BMP). (see textbook pp. 28, Eq. 2.40 for the definition of
class C(A, 0, z) (autonomous) CNN ). Hint: Zero-input means that values of the B template should be
zero. Output should be a binary image and this determines condition for A00. The two templates are
same except one value.
Task 1 – producing output 1
Network settings
Input
Non definied
Initial State
The Picture
Boundary Condition
Fixed 0
input
output 1
Your first template:
0
0
0
A=
0
2
0
0
0
0
B=
0
0
0
0
0
0
0
0
0
z=
-0,25
Task 2 – producing output 2
Network settings
Input
Non definied
Initial State
The Picture
Boundary Condition
Fixed 0
input
output 2
Your second template:
0
0
0
A=
0
2
0
0
0
0
B=
0
0
0
0
0
0
0
0
0
z=
0,5
Part A-3: Design templates solving the following operations
Task 1 – producing output 1: Given the two images P1 (LIFE_1.BMP) and P2 (CONC1.BMP), find a
5
template that produces the output 1. Verify your answer using the simulator and specify template and
proper initial settings.
input1 – P1
input2 – P2
Your template to obtain output 1
0
0
0
A=
0
1
0
B=
0
0
0
output 1
0
0
0
0
1
0
0
0
0
z=
1
Network settings
Input
Initial State
Boundary Condition
P1
P2
Zero-flux
Task performed by template is: I thin that this is a simple addition (or UNION). So, if one of them is
black, then I should make black. If white, then I make white. So I need only the middle pixels.
Task 2 – producing output 2: Given the two images P1 (CHINEESE.BMP) and P2 (CONC1.BMP), find a
template that produces the output 2.
input1 – P1
input2 – P2
Your template to obtain output 2
0
0
0
A=
0
2
0
B=
0
0
0
output 2
0
0
0
0
1
0
0
0
0
z=
-1
Network settings
Input
P1
Initial State
P2
Boundary Condition
0
6
Name:
Midterm Project – Part B
Issued on: March 16, 2006 – Due date: March 31, 2006
The Midterm Project has 2 parts (Part A and Part B). This document contains Part B.
There are several different ways to run CNN templates on the CNN simulator “CANDY”. “Template Runner” is the
easiest one among them. All of the following problems can be run on the “template runner”. Images can be found in
directory \PicLib of Candy or downloadable from the course web page.
Part B
Design CNN templates to implement the following five (5) given tasks. You must document your design
procedure to receive credit, which should include the following:

Based on the task, determine the template format, e.g. which are the zero and non-zero template
elements, what symmetries should there be, or what elements should be identical?

Determine the settings of the network (input, initial state, boundary) and also the simulation parameters
(simulation time, time step, sampling)

Determine the set of inequalities on the template elements required by the task.

Find the solution of the system of inequalities.

Test your template with the Candy simulation system.

Optimize your template (if applicable) with the TEMMASTER program.
Task 1: Design a single template which implements a simple morph transition.
Example:
herring.bmp (P1)
snapshot 1
snapshot 2
snapshot 3
median.bmp (P2)
Task: Template implements a simple morph transition between two images.
Given: two grayscale images, P1 and P2
Input:
U(t) = P2
Initial State:
X(0) = P1
Boundary Conditions: zeroflux
Output: Y(t) = a transition from P1 to P2, Y(∞) = P2 (grayscale image)
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Hint: Terms “image morph” or “image morphing” define an image processing technique as the movement
(transition) from one image into another one. It is typically used in animation tools and most common examples
can be seen in music videos. There are lots of examples of one face morphing into another face (you can create
humorous examples and show them to your friends). This can be implemented as an uncoupled template and
morphing will take place during the CNN transient. You should specify an “Output Sampling” value in the
simulator to be able to see this transient. In the example shown above snapshots are the consecutive steps of this
transient. Definition of uncoupled template can be found in the textbook (paragraph 2.2.5, Fig. 2.24). Also you
do not need coupling among the input cells either. This means you should specify only two template values,
namely, a00 and b00. Keep in mind that the input is grayscale and also the output (steady state of the CNN)
should remain in the grayscale region [-1, 1]. This means if you sketch the DP plot it cannot have stable
solutions in the saturated regions. Also avoid semi-stable solution in the grayscale region otherwise the final
steady state won’t be P2. Simulator: to specify two images you should set the TemRunner input and state
parameters to Picture and Picture2 before starting the template execution.
Task 2:
Example:
Deleting Short Vertical lines.
Input (P1)
Output (P2)
LINCUT7V.bmp
Task: Design a 3x1 template which deletes vertical lines of length 1, 2, or 3 pixels in black and white
images. Longer vertical lines should have one and only one pixel removed from both ends.
Given: a binary image, P1
Output: Y(∞) = P2 (binary image) showing only long vertical lines.
Hint: Check template library for a similar template. Description of templates can be found at web page:
http://lab.analogic.sztaki.hu/Candy/csl.html. First, try to erase 1- and 2-pixel long vertical lines (uncoupled
template). To erase even 3-pixel long vertical lines you might need the propagating property of the A template.
A similar template is DELVERT1.tem (this is an example for uncoupled template). But this template erases
vertical lines even longer lines than 3 pixels. You want to keep them except up to 3-pixel long ones. (Do not just
copy template elements from DELVERT1.tem to accomplish your task.) First, to erase 1- and 2- pixel long
vertical lines you need to keep points that belong lines at least 3-pixel long ones. This will give you values for
the B template, b-1,0 and b1,0. This will be an uncoupled template. Now you want to erase even 3-pixel long lines.
Therefore you must modify the A template values a-1,0 and a1,0. Other template values (B, z) might remain the
same.
8
Task 3: Design template(s) locating the main groups of points.
Example:
Input (P1)
Output (P2)
PointSet.bmp (course web page)
Task: Given an image (P1) similar to the one shown above, locate the main groups of points (similar
to output image P2).
Given:
a binary image, P1
Output:
Y(∞) = P2 (binary image) showing main groups of points
Hint: To solve this problem non-local information is needed. You have to use the propagating property of the A
template. Where the average pixel number is large enough, there is a group. Apply blurring, then some kind of
thresholding method. If you cannot solve this task with a single template you can run two templates, the second
one should be applied on the sub-result of the first template. See templates e.g. DIFFUS.tem, AVERAGE.tem,
and THRES.tem in directory \Temlib of Candy. Do not forget to specify the proper running times.
Task 4: Design propagating type template which deletes unclosed lines.
Example:
Input (P1) ClosedLines.bmp (course web page)
Output (P2)
Task: Given a binary image similar to the one shown above, design a propagating type template that
repeatedly deletes the last pixels of a line. In this way, at the end of the transient, the unclosed lines
(which have line ends) will be deleted, but the closed loops will remain unchanged. We assume that
there are non-crossing single pixel wide lines on a black-and-white image.
Given: a binary image, P1 (non-crossing, single pixel wide lines)
Output: Y(∞) = P2 (binary image) showing only closed loops.
Hint: You need to use the propagating property of the A template. Check the template library for a similar
template. Web page: http://lab.analogic.sztaki.hu/Candy/csl.html. Do not forget to specify the proper running
time (does it depends on the image content?). Template values: a0,0; a-1,0; a0,-1; a0,1 and a1,0; b0,0 and z.
Task 5: Local Logic Template design.
9
Design a template which can extract the (a), (b), and (c) local patterns, i.e. the output will be black if,
and only if, the local input neighborhood is one of these patterns. The dash means “don’t care.”
(a)
(b)
(c)
Example:
Input (P1) – LocalLogic.bmp (course web page)
Output (P2)
Given: a binary image, P1
Output: Y(∞) = P2 (binary image) showing only centers of matched patterns.
Hint: Optimize your template with the TEMMASTER program. This will be an uncoupled template.
10
Task 1:
The simpliest way to change the picture1, with picture2 when we change only the centeral pixel in B
template, and I shouldn’t use feedback mechanism in template A.
When I search a better algorithm, I found the morph template, which is same as mine.
Template:
0
0
0
0
0
0
A=
0
0
0
B=
0
1
0
z=
0
0
0
0
0
0
0
Network settings
Input
Picture2
Initial State
Picture1
Boundary Condition Fixed 0
Simulator settings
Running time
4,6
Time step
0,01
Output sampling
1
LÉGYSZI AZ ÁTMENETET CSINÁLD MEG, HOGY NE
LEGYEN FELTŰNŐ!!!
I make some snapshot with mine template (running 4,3, with step 0,1):
Tau: 0
1
2
3
4,6
Target
We can see that, mine template is good.
I tried to optimize the Temmaster, but I get a very bad A template, which was not efficient.
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Task 2:
The task is to design a template, which deletes the vertical lines of length 1-3 pixels, and one pixel
removed from both ends, where the line is longer than 3 pixel.
My algorithm is depend from bias. If I modified even with small value of the bias, than I can’t get the
proper result, or worse. The ideal bias value is -2, which came from the erosion template. We want to
delete vertical line pixels, that’s why the not zero values of the A and B matrix have a vertical
structure.
INNEN KISZEDTEM NÉMI RÁM
SPECIFIKUS RÉSZT.
The result was good, but I get a tip, and I tried to calc with center:
Template:
0
1
0
0
1
0
A=
0
1
0
B=
0
1
0
z=
0
1
0
0
1
0
Network settings
Input
Picture
Initial State
Arbitrary
Boundary Condition Fixed 0
-2
Simulator settings
Running time
2
Time step
0,1
Output sampling
1
So this is better, because the running time is half, so its, the good:

12
Task 3:
In First time try to make step-by-step the hint with random tau. The Candy is inbuilt DIFFUS template was
realy useful, but the AVERAGE result was small.
The TRESHOLD was very strange, but I saw that, in small tau was near the output. So I decided that, I tried to
change the bias of this template (original was -0.4).
I tried many bias up to 0.5 (with tau=3):
Second, I tried to tune the good settings, first the Diffus:
Template:
0,1 0,15 0,1
A = 0,15
0
0,15
0,1 0,15 0,1
Network settings
Input
Undef
Initial State
Picture
Boundary Condition 0
B=
0
0
0
0
0
0
0
0
0
z=
0
Simulator settings
Running time
16
Time step
0,1
Output sampling
1
13
Template:
0
A=
0
0
0
2
0
0
0
0
Network settings
Input
Undef
Initial State
Picture
Boundary Condition 0
B=
0
0
0
0
0
0
0
0
0
z=
0,55
Simulator settings
Running time
5
Time step
0,1
Output sampling
1
It’s near to the P2 task, but the result is depend from THRES’ is bias and from the diffusion.
I tried smaller bias:
AZ ÁBRÁKAT A FENTIEK SZERINT KÉSZITSD
ÚJRA (az enyém valamivel finomabb…)
The result of mine set:
Mine
Task
14
Task 4:
This task was quite simple, because dr. Tamás Roska given the solution, when speak about dr. Ákos
Zarándy, and write his template, which can explore the not connected lines.
So this template is delete the connectless ends of the lines. The template running is depnds from the
longest line.
Template:
0
A=
1
0
1
2
1
0
1
0
B=
0
0
0
Network settings
Input
Picture
Initial State
Picture
Boundary Condition Fixed 0
0
0.6
0
0
0
0
z=
-1,5
Simulator settings
Running time
5
Time step
0,1
Output sampling
1
LÉGYSZI AZ ÁTMENETET CSINÁLD ÚJRA, HOGY NE
LEGYEN FELTŰNŐ, köszi :).
Step-by-step:
0
1
2
3
4
5
After 100:
So, the template is OK.
15
Task 5:
Ha átfogalmazod a mondatokat, egy az egyben be
is adhatod.
The A template is simple, but the B template is designed, so that the b(-1,0) = -2 because this pixels
should be white. The others should be black, these are greater than 0 in B, and the null means don’t
care.
The Temmaster gives a good result:
Template:
0
A=
0
0
0
1
0
0
0
0
B=
Network settings
Input
Picture
Initial State
Arbitrary
Boundary Condition Zero-flux
1
2
0
-2
2
2
1
2
0
z=
-9
Simulator settings
Running time
1
Time step
0.1
Output sampling
1

16