A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Unsupervised texture segmentation using Gabor filters
Pattern Recognition
Handbook of pattern recognition & computer vision
Handbook of pattern recognition & computer vision
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Texture Classification by Wavelet Packet Signatures
IEEE Transactions on Pattern Analysis and Machine Intelligence
Log-Polar Wavelet Energy Signatures for Rotation and Scale Invariant Texture Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICIP '97 Proceedings of the 1997 International Conference on Image Processing (ICIP '97) 3-Volume Set-Volume 3 - Volume 3
Analysis of multiscale products for step detection and estimation
IEEE Transactions on Information Theory
Texture segmentation using filters with optimized energy separation
IEEE Transactions on Image Processing
Statistical texture characterization from discrete wavelet representations
IEEE Transactions on Image Processing
Comparison of texture features based on Gabor filters
IEEE Transactions on Image Processing
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In this paper, we propose a methodology for the optimal design of bi-orthogonal wavelet basis for maximum possible texture discrimination. The objective of this optimization process is to obtain maximum separation between local features of the texture image at different resolution scales. There are several applications which may not require reconstruction of signal from its transformed coefficients such as texture analysis, remote sensing, medical diagnostics etc. Therefore, for such applications, features are extracted at different frequency resolution scales and condition for perfect reconstruction can be relaxed. In this research work, we propose a methodology for the optimal design of biorthogonal wavelet bases. Our objective function is based on maximization of distinguishibility function involving the computation of finer details subject to some wavelet constraints. Classification results of optimized wavelet were compared with the existing maximally flat biorthogonal wavelet families which shows that the results obtained are superior in terms of texture discrimination.