feature selection using dynamic binary particle swarm...
TRANSCRIPT
Feature Selection using Dynamic Binary Particle
Swarm Optimization for Arabian Horse
Identification System
Presented by: Samar Ibrahem Youssef
Annual Conference of the Institute of Studies and Statistical Research 2017
Workshop in Intelligent Systems and Applications
MSc Student , Information Technology Department,
Faculty of Computers and Information,
Cairo University
Scientific Research Group in Egypt
27/12/2017
Outline
27/12/2017
Introduction.
Problem Statement.
Proposed Methodology:
Phase I : Pre-processing.
Phase II : Segmentation.
Phase III : Feature Extraction.
Phase IV : Feature Selection.
Conclusions & Future Work.
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Outline
27/12/2017
Introduction.
Problem Statement.
Proposed Methodology:
Phase I : Pre-processing.
Phase II : Segmentation.
Phase III : Feature Extraction.
Phase IV : Feature Selection.
Conclusions & Future Work.
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Introduction
Need for Arabian Horse Identification
Biosecurity.
Retrieval after theft.
Fairness in competition.
Medical record management.
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Outline
27/12/2017
Introduction.
Problem Statement.
Proposed Methodology:
Phase I : Pre-processing.
Phase II : Segmentation.
Phase III : Feature Extraction.
Phase IV : Feature Selection.
Conclusions & Future Work.
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Problem Statement
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Tattooing
Ear Tagging
Traditional Identification Methods
Branding
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Problem Statement
Using Biometric identifiers (contactless methods) for identification
and getting rid of harmful identification methods .
Muzzle Print IRIS
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Problem Statement
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Outline
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Introduction.
Problem Statement.
Proposed Methodology:
Phase I : Pre-processing.
Phase II : Segmentation.
Phase III : Feature Extraction.
Phase IV : Feature Selection.
Conclusions & Future Work.
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Phase I: Pre-processing
Pre-Processing
Image Resizing
Convert to grayscale
Contrast Stretching
Noise Removal
Input Image Processed Image
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Outline
27/12/2017
Introduction.
Problem Statement.
Proposed Methodology:
Phase I : Pre-processing.
Phase II : Segmentation.
Phase III : Feature Extraction.
Phase IV : Feature Selection.
Conclusions & Future Work.
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Phase II : Segmentation
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Pre-Processing
Image Resizing
Convert to grayscale
Contrast Stretching
Noise Removal
Segmentation
Otsu-IFOA
Opening & Masking
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Phase II: Segmentation(continued)
Otsu-IFOA Segmentation
Binary Mask Thresholded Image Segmented Area
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Processed Image
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Outline
27/12/2017
Introduction.
Problem Statement.
Proposed Methodology:
Phase I : Pre-processing.
Phase II : Segmentation.
Phase III : Feature Extraction.
Phase IV : Feature Selection.
Conclusions & Future Work.
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Phase III: Feature Extraction
Pre-Processing
Image Resizing
Convert to grayscale
Contrast Stretching
Noise Removal
Segmentation
Otsu-FOA
Opening & Masking
Feature Extraction
Gabor Filtering
TDCT
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Phase III :Feature Extraction
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(Gabor Filtering)
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Phase III :Feature Extraction
(Gabor Filtering)
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Phase III: Feature Extraction(TDCT)
Pre-Processing
Image Resizing
Convert to grayscale
Contrast Stretching
Noise Removal
Segmentation
Otsu-FOA
Opening & Masking
Feature Extraction
Gabor Filtering
TDCT
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Phase IV: Feature Selection
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Pre-Processing
Image Resizing
Convert to grayscale
Contrast Stretching
Noise Removal
Segmentation
Otsu-FOA
Opening & Masking
Feature Extraction
Gabor Filter
TDCT
Feature Selection
DBPSO
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The Flowchart of DBPSO
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DBPSO Results
We have 1800 extracted
features .
265 selected features using
DBPSO.
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Outline
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Introduction.
Problem Statement.
Proposed Methodology:
Phase I : Pre-processing.
Phase II : Segmentation.
Phase III : Feature Extraction.
Phase IV : Feature Selection.
Conclusions & Future Work.
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Conclusions & Future work
Automatic Periocular region segmentation using
Otsu-IFOA.
Feature Extraction(Gabor Filter + TDCT).
Feature Selection using DBPSO.
Selected Features can be used for Arabian Horse
Identification System.
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