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

TOPICS

• Introduction

• Simulation Tool (MATLAB/Simulasi)

Functions used

• Background Estimation

•Application of 2-D Cross correlation Algorithm

•Application of SAD Algorithm

• Conclusion

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INTRODUCTION

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• 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.

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USAGE OF MATLAB TOOLS

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• 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.

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Contd..

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• 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.

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FUNCTIONS USED

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• 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( );

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SELECTION PROCESS

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• 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.

BACKGROUND ESTIMATION

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• 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.

2-D CROSS CORRELATION

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• 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.

2-D Cross Correlation Algorithm

1⋅8+8⋅1+15⋅6+7⋅3+14⋅5+16⋅7+13⋅4+

20⋅9+22⋅2=585

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SUM OF ABSOLUTE DIFFERENCE

ALGORITHM

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• 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.

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SAD ALGORITHM

Original Image On Application of SAD Algorithm

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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.

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Thank you

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Queries ???

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