TEL AVIV UNIVERSITY
FACULTY OF ENGINEERING
SCHOOL OF ELECTRICAL ENGINEERING
Computer Vision
Final Project
Balls recognition
2011
Blok Diagram
Images
RGB images uint8
RGB to HSV
H/S double
Calc Euclidean Dist
for Red/Green/Yellow
{Hue and Sat channels}
R/G/Y double
Apply Euclidean Dist
Threshold
R/G/Y logical
Erosion
R/G/Y logical
Adaptive Color
Filter
R/G/Y logical
OR
Color Mask logical
Filling
Holes
Color Mask logical
Color Mask logical
Val double
Mult
Val (ROI) double
Erosion
Of Artificial edges
mask
Edge Detection
(Canny)
Edge detection (ROI) double
Radius of Max object double
Edge detection (ROI) double
Kernels
Kernels logical
Label Matrix double
Mult
Edge detection (ROI) double
Loop
Control
Labeling
Eroded Color Mask logical
Adaptive XNormCorr
(Run only on Kernels that less of radius
of max object)
Coor images double
Find Coordinates of Max
Corr value
Find the best Kernel
Flag
Edge detection (ROI) double
(After removing previous good candidate)
Best Kernel Mask logical
Pre-processing
Flag
Condition 1: Inside the kernel all 3 colors must present
Bad candidate
Pass
Current Candidate
Max Corr Val = 0
Flag
Fail
Bad candidate
Condition 2: Inside the kernel must be only one object
Pass
Remove ROI of
current candidate
Bad candidate
Flag
Pass
Plot Results
Condition 2: Relative area of the Object to size of the
kernel must be very high
Good candidate
Calc Radius of
Max object
Dilation
Algorithm
I will take one of the image as example for explanation my
algorithm
1.
Load original images
Original
2.
Load Ball Kernels
Explanation:
 In my algorithm I use different size of Kernels to
perform correlation with the image for reason to
find the optimal ball looks like objects
3.
Convert from RGB to HSV
4.
Calculate Euclidean distance
Explanation:
 Here I use Euclidean distance calculation for each
color separately (Red, Green, and Yellow). The
colors of the ball. As result I receive images where
the darker pixel means that this pixel is closer to
relative color
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5.
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Applying threshold on Euclidean distance images for
each color separately
Color Recognition RED
6.
400
Color Recognition GREEN
Color Recognition YELLOW
Erosion
Explanation:
 Remove the noise after color recognition
Color Recognition RED - eroded
7.
Color Recognition GREEN - eroded
Color Recognition YELLOW - eroded
Adaptive color filter
Explanation:
 Here I apply my adaptive color filter. The filter
relates to all 3 colors and performs scanning on
the window of 20 pixels. The filter scan each pixel
in each color and looks if there is another colors in
the window, if not – remove it.
Color Recognition RED - eroded
Color Recognition GREEN - eroded
Color Recognition YELLOW - eroded
8.
Combine all 3 colors together
Color Recognition Adaptive
9.
Fill the holes
Filling the holes - adaptive
10. Dilation
Explanation:
 Perform dilation to be sure that I’m not missing the
borders of the object
Dilation After filling - adaptive
11. Multiply Mask after color recognition with Gray level
image (Val from HSV)
Color Recognition ROI Fill+Dilate - adaptive
12. Erosion on {10}
Explanation:
 In this step I perform erosion on dilated mask in
the reason that in future step I will perform edge
detection and all artificial edges of ROI I need to
remove by applying this mask
Color Recognition Erode on ROI
13. Edge detection (Canny) on {11}
Edge of ROI
14. Multiplying {12} with {13} - removing artificial edges
Edge of eroded ROI
15. Performing labeling of {12}
Explanation:
 Here I perform labeling for future using and
calculation the radius of max object
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16. Performing adaptive xnormcorr with Ball Kernels {2}
Explanation:
 Here I use the radius of the max object from {15}
to determine the max kernel size that I need to
apply, it reduces drastically time running of the
code
As output I receive vector of max corr values for
each kernel
MaxCorrVal = 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 58
58 61 67 63 60 102 93 97 99
93 94 87 94 80 94 89 81 78
80 83 78 93 87 73 98 120 95
85 76 81 77 75 76 72 72 71
77
Part of the values is zeros as result of applying
adaptive algorithm
17. Find the best Kernel and find coordinates of the max
corr val
BallCoord =
y: 297
x: 358
Correlation with Best kernel
18. Check 3 condition for valid candidate
Explanation:
Condition 1: Inside the kernel all 3colors must present
Condition 2: Inside the kernel must be only one object
Condition 2: Relative area of the Object to size of the
Kernel must be very high
If all conditions pass – save results and go to {19}, if
fail – MaxCorrVal(i) = 0, take the next candidate.
19. After finding with success one ball remove the area of
this ball and repeat from {16} to find another ball
Edge of eroded ROI
Before removing
After removing
20. Plot/Save the results
Explanation:
After finding each ball I have few information about it:
 BallCoord – x,y coordinates of the balls
 BestKernelInd – index of best kernel for each ball
 BestKernel – best kernel for each ball
I combine all those parameters for plotting Kernels on
right position of the balls relative to founded
coordinates
Ball #1 - Kernel #43
Ball #2 - Kernel #47