tailieunhanh - Báo cáo hóa học: "Texture Classification Using Sparse Frame-Based Representations"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Texture Classification Using Sparse Frame-Based Representations | Hindawi Publishing Corporation EURASIP Journal on Applied Signal Processing Volume 2006 Article ID 52561 Pages 1-11 DOI ASP 2006 52561 Texture Classification Using Sparse Frame-Based Representations Karl Skretting and John Hakon Hus0y Department of Electrical and Computer Engineering University of Stavanger 4036 Stavanger Norway Received 31 August 2004 Revised 20 April 2005 Accepted 2 June 2005 A new method for supervised texture classification denoted by frame texture classification method FTCM is proposed. The method is based on a deterministic texture model in which a small image block taken from a texture region is modeled as a sparse linear combination of frame elements. FTCM has two phases. In the design phase a frame is trained for each texture class based on given texture example images. The design method is an iterative procedure in which the representation error given a sparseness constraint is minimized. In the classification phase each pixel in a test image is labeled by analyzing its spatial neighborhood. This block is represented by each of the frames designed for the texture classes under consideration and the frame giving the best representation gives the class. The FTCM is applied to nine test images of natural textures commonly used in other texture classification work yielding excellent overall performance. Copyright 2006 Hindawi Publishing Corporation. All rights reserved. 1. INTRODUCTION Most surfaces exhibit texture. For human beings it is quite easy to recognize different textures but it is more difficult to precisely define a texture. Under all circumstances a texture may be regarded as a region where some elements or primitives are repeated and arranged according to a placement rule. Tuceryan and Jain 1 list more possible definitions and give a comprehensive overview of texture classification. Possible applications can be grouped into 1 texture analysis that is finding some appropriate properties for a texture 2 texture .