Extraction of noise tolerant, gray-scale transform and rotation invariant features for texture segmentation using wavelet frames

TLDR: A texture feature extraction scheme at multiple scales is proposed and the issues of rotation and gray-scale transform invariance as well as noise tolerance of a texture analysis system are discussed.

Abstract: In this paper, we propose a texture feature extraction scheme at multiple scales and discuss the issues of rotation and gray-scale transform invariance as well as noise tolerance of a texture analysis system. The nonseparable discrete wavelet frame analysis is employed which gives an overcomplete wavelet decomposition of the image. The texture is decomposed into a set of frequency channels by a circularly symmetric wavelet filter, which in essence gives a measure of edge magnitudes of the texture at different scales. The texture is characterized by local energies over small overlapping windows around each pixel at different scales. The features so extracted are used for the purpose of multi-texture segmentation. A simple clustering algorithm is applied to this signature to achieve the desired segmentation. The performance of the segmentation algorithm is evaluated through extensive testing over various types of test images.