Texture discrimination by Gabor functions
Biological Cybernetics
Multichannel Texture Analysis Using Localized Spatial Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Design and Use of Steerable Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised texture segmentation using Gabor filters
Pattern Recognition
The nature of statistical learning theory
The nature of statistical learning theory
Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Texture Segmentation in a Deterministic Annealing Framework
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM Computing Surveys (CSUR)
A Trainable System for Object Detection
International Journal of Computer Vision - special issue on learning and vision at the center for biological and computational learning, Massachusetts Institute of Technology
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Introductory Techniques for 3-D Computer Vision
Introductory Techniques for 3-D Computer Vision
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Face Recognition Using Principal Component Analysis of Gabor Filter Responses
RATFG-RTS '99 Proceedings of the International Workshop on Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems
Comparison of Texture Features Based on Gabor Filters
ICIAP '99 Proceedings of the 10th International Conference on Image Analysis and Processing
Optimal Gabor filters for texture segmentation
IEEE Transactions on Image Processing
On-Road Vehicle Detection: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
MutualBoost learning for selecting Gabor features for face recognition
Pattern Recognition Letters
MutualBoost learning for selecting Gabor features for face recognition
Pattern Recognition Letters - Special issue on vision for crime detection and prevention
Information theory for Gabor feature selection for face recognition
EURASIP Journal on Applied Signal Processing
On selecting Gabor features for biometric authentication
International Journal of Computer Applications in Technology
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Despite the considerable amount of research work on the applicationof Gabor filters in pattern classification, their design and selectionhave been mostly done on a trial and error basis. Existing techniques areeither only suitable for a small number of filters or less problem-oriented.A systematic and general evolutionary Gabor filter optimization (EGFO)approach that yields a more optimal, problem-specific, set of filters is proposedin this study. The EGFO approach unifies filter design with filter selectionby integrating Genetic Algorithms (GAs) with an incremental clusteringapproach. Specifically, filter design is performed using GAs, a globaloptimization approach that encodes the parameters of the Gabor filters ina chromosome and uses genetic operators to optimize them. Filter selectionis performed by grouping together filters having similar characteristics(i.e., similar parameters) using incremental clustering in the parameterspace. Each group of filters is represented by a single filter whose parameterscorrespond to the average parameters of the filters in the group. Thisstep eliminates redundant filters, leading to a compact, optimized set of filters.The average filters are evaluated using an application-oriented fitnesscriterion based on Support Vector Machines (SVMs). To demonstrate theeffectiveness of the proposed framework, we have considered the challengingproblem of vehicle detection from gray-scale images. Our experimentalresults illustrate that the set of Gabor filters, specifically optimized for theproblem of vehicle detection, yield better performance than using traditionalfilter banks.