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Towards Efficient Texture Classification and Abnormality Detection
Monadjemi Amir
Department of Computer Science
Research output
:
Other contribution
›
PhD thesis (not Bristol)
Overview
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Dive into the research topics of 'Towards Efficient Texture Classification and Abnormality Detection'. Together they form a unique fingerprint.
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Engineering
Texture Classification
100%
Feature Extraction
50%
Classification Performance
50%
Multiscale
50%
Texture Feature
50%
Generality
25%
Classification Method
25%
Processing Method
25%
Texture Analysis
25%
Image Processing
25%
Cooccurrence Matrix
25%
Building Element
25%
High Resolution
25%
Computational Cost
25%
Gabor Filter
25%
Phase Composition
25%
Higher-Order Statistics
25%
Computer Science
Texture Classification
100%
Classification Performance
50%
Texture Feature
50%
Hadamard Transform
50%
Feature Extraction
50%
Texture Analysis
25%
Processing Approach
25%
Extensive Training
25%
Image Processing
25%
Higher-Order Statistics
25%
classification approach
25%
Computational Cost
25%
Classification Method
25%
Processing Method
25%
Occurrence Matrix
25%
Computer Vision
25%
Mathematics
Signal Processing
100%
Order Statistics
33%
Computational Cost
33%
Statistical Method
33%
Composition Method
33%
Algorithm Analysis
33%
Image Processing
33%
Generality
33%
Hadamard Transform
33%
Occurrence Matrix
33%
Walsh-Hadamard Transform
33%
Classification Method
33%
Earth and Planetary Sciences
Signal Processing
100%
Pattern Recognition
66%
Gabor Filter
33%
Computer Vision
33%
Image Processing
33%