A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multichannel Texture Analysis Using Localized Spatial Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture Classification Using Dominant Wavelet Packet Energy Features
SSIAI '00 Proceedings of the 4th IEEE Southwest Symposium on Image Analysis and Interpretation
Integrated active contours for texture segmentation
IEEE Transactions on Image Processing
Texture classification and segmentation using wavelet frames
IEEE Transactions on Image Processing
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Wavelet and wavelet packet decompositions have been proven to be very effective in analyzing various types of signals and images. One useful type of analysis is image and texture classification. Such processing requires the analysis framework to be invariant to changes in scale, translation and other types of deformations. We deal in this context with the shift variance of the discrete wavelet transform. Several methods have been proposed to cope with this problem. We extend the shift invariant wavelet frame method, described in previous studies, to "shift invariant wavelet frame packets", and greatly reduce its computational complexity. In the one-dimensional case, our method maintains O(ND) computation steps (where D is the decomposition depth and N is signal length), when either traditional or wavelet packet decomposition tree is used, instead of O(ND) and O(N2D) respectively.