EXTRACTION OF NOISE TOLERANT, GRAY-SCALE TRANSFORM AND ROTATION INVARIANT FEATURES FOR TEXTURE SEGMENTATION USING WAVELET FRAMES
- 1 May 2008
- journal article
- research article
- Published by World Scientific Pub Co Pte Ltd in International Journal of Wavelets, Multiresolution and Information Processing
- Vol. 06 (03), 391-417
- https://doi.org/10.1142/s0219691308002252
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.Keywords
This publication has 15 references indexed in Scilit:
- M-BAND WAVELETS: APPLICATION TO TEXTURE SEGMENTATION FOR REAL LIFE IMAGE ANALYSISInternational Journal of Wavelets, Multiresolution and Information Processing, 2003
- Multiresolution gray-scale and rotation invariant texture classification with local binary patternsIeee Transactions On Pattern Analysis and Machine Intelligence, 2002
- Brief review of invariant texture analysis methodsPattern Recognition, 2001
- Classification of Rotated Textures using Overcomplete Wavelet FramesIETE Journal of Research, 2000
- Rotation-invariant texture classification using a complete space-frequency modelIEEE Transactions on Image Processing, 1999
- EFFICIENT ROTATION INVARIANT TEXTURE FEATURES FOR CONTENT-BASED IMAGE RETRIEVALPattern Recognition, 1998
- Texture classification and segmentation using wavelet framesIEEE Transactions on Image Processing, 1995
- Rotation and gray scale transform invariant texture identification using wavelet decomposition and hidden Markov modelIeee Transactions On Pattern Analysis and Machine Intelligence, 1994
- Texture classification by wavelet packet signaturesIeee Transactions On Pattern Analysis and Machine Intelligence, 1993
- Classification of rotated and scaled textured images using Gaussian Markov random field modelsIeee Transactions On Pattern Analysis and Machine Intelligence, 1991