ICIECA 2014 Paper

1. KONGU ENGINEERING COLLEGE
PERUNDURAI, ERODE-638 052
ISOLATION OF DEAD CHICKEN IN POULTRY
BY IMAGE PROCESSING
PRESENTED BY:
S.SUNIL SWAROOP (III-BE.-ECE)
J.SINGAARA VELAVAR (III-BE.-ECE)
12/20/2014 1ICIECA 2014

2. TOPICS
• Introduction
• Simulation Tool (MATLAB/Simulasi)
 Functions used
• Background Estimation
• Application of 2-D Cross correlation Algorithm
• Application of SAD Algorithm
• Conclusion
12/20/2014 2ICIECA 2014

3. INTRODUCTION
12/20/2014
3
• Poultry business is a major contributor for the INDIAN
economy.
• Already feeding mechanisms and collection of eggs inside
the cabin is automated.
• The major concern with the poultry is the menace of
diseases that is often affecting the chicken.
• The problem is that, the disease quickly spreads among the
chickens and affect others.
• When it is done manually, it is a time consuming process.
ICIECA 2014

4. USAGE OF MATLAB TOOLS
12/20/2014 4
• Automate the poultry process by continuous monitoring the
chickens using image processing.
• MATLAB consist of various toolbox among which Image
Processing is one.
• Here the image is in the form of a Matrix.
• The Image Processing toolbox is a collection of functions.
ICIECA 2014

5. Contd..
12/20/2014 5
• There are 4 basic types of images in the toolbox.
• Intensity image - Value corresponding to
brightness/darkness of the pixel.
• Binary image - Only color pixel black or white.
• Indexed image - Size differs from the first.
• RGB image - Format of color images.
ICIECA 2014

6. FUNCTIONS USED
12/20/2014 6
• Pre-processing - Remove blur.
• Conversion to gray scale – rgb2gray( );
• Histogram equalization - histeq
• Intensity adjustments – imadjust( );
• 2-D cross-correlation - xcorr2( );
• Absolute difference of two images – imabsdiff( );
ICIECA 2014

7. SELECTION PROCESS
12/20/2014 ICIECA 2014 7
• Acquisition of the image through the webcam connected via
USB port.
• Resolution of the camera – Based on the length of the cabin.
• We had applied two algorithms :
– 2-D Cross correlation Algorithm.
– Sum of Absolute Difference Algorithm.

8. BACKGROUND ESTIMATION
12/20/2014 ICIECA 2014 8
• Static background motion detection:
– No object in the camera is stored in a bitmap.
– If a pixel is different it is marked as white, if not it is
left black.
• Dynamic motion detection:
– Frames that are received together are compared i.e.,
Frame differentiating.

9. 2-D CROSS CORRELATION
12/20/2014 ICIECA 2014 9
• First image captured is kept as threshold.
• After a period of ‘n’ times another image is taken.
• First the 2 images were sub divided into 4 equal parts
each – to increase the accuracy.
• These produces four values.
• The maximum value of correlation will be used as
reference and compared with threshold value.

10. 2-D Cross Correlation Algorithm
1⋅8+8⋅1+15⋅6+7⋅3+14⋅5+16⋅7+13⋅4+
20⋅9+22⋅2=585
12/20/2014 10ICIECA 2014

11. SUM OF ABSOLUTE DIFFERENCE
ALGORITHM
12/20/2014 11
• Is mathematically represented
D (t) = 1/N ∑|I (ti) – I (tj)|
• A image is taken at ti time.
• Another image is taken at time tj.
• In ideal case D (t) is zero.
• The difference between the images – isolate the dead
chicken.
ICIECA 2014

12. SAD ALGORITHM
Original Image On Application of SAD Algorithm
12/20/2014 12ICIECA 2014

13. CONCLUSION
(REASON FOR THE USAGE OF TWO
ALGORITHMS)
• Drawbacks in using 2-D cross correlation algorithm which
gives approximate output.
• On using the second algorithm we can have a accurate
output.
• Considering this practical problem an idea is suggested.
• Will fulfill the needy when implemented.
12/20/2014 ICIECA 2014 13

14. Thank you
12/20/2014 14ICIECA 2014

15. Queries ???
12/20/2014 ICIECA 2014 15