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: Research Article Learning How to Extract Rotation-Invariant and Scale-Invariant Features from Texture Images | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2008 Article ID 691924 15 pages doi 2008 691924 Research Article Learning How to Extract Rotation-Invariant and Scale-Invariant Features from Texture Images Javier A. Montoya-Zegarra 1 2 Joao Paulo Papa 1 2 Neucimar J. Leite 2 Ricardo da Silva Torres 2 and Alexandre X. Falcao2 1 Computer Engineering Department Faculty of Engineering San Pablo Catholic University Av. Salaverry 301 Vallecito Arequipa Peru 2 Institute of Computing The State University of Campinas 13083-970 Campinas SP Brazil Correspondence should be addressed to Javier A. Montoya-Zegarra jmontoyaz@ Received 2 October 2007 Revised 1 January 2008 Accepted 7 March 2008 Recommended by C. Charrier Learning how to extract texture features from noncontrolled environments characterized by distorted images is a still-open task. By using a new rotation-invariant and scale-invariant image descriptor based on steerable pyramid decomposition and a novel multiclass recognition method based on optimum-path forest a new texture recognition system is proposed. By combining the discriminating power of our image descriptor and classifier our system uses small-size feature vectors to characterize texture images without compromising overall classification rates. State-of-the-art recognition results are further presented on the Brodatz data set. High classification rates demonstrate the superiority of the proposed system. Copyright 2008 Javier A. Montoya-Zegarra et al. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. 1. INTRODUCTION An important low-level image feature used in human perception as well as in recognition is texture. In fact the study of texture has found several applications ranging from texture segmentation 1 to texture classification 2 synthesis
