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 Feature Point Detection Utilizing the Empirical Mode Decomposition | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2008 Article ID 287061 13 pages doi 2008 287061 Research Article Feature Point Detection Utilizing the Empirical Mode Decomposition Jesmin Farzana Khan 1 Kenneth Barner 2 and Reza Adhami1 1 Department of Electrical and Computer Engineering University of Alabama in Huntsville Huntsville AL 35899 USA 2 Department of Electrical and Computer Engineering University of Delaware Delaware DE 19716 USA Correspondence should be addressed to Jesmin Farzana Khan khanj@ Received 22 June 2007 Revised 18 January 2008 Accepted 3 March 2008 Recommended by Ray Zhang This paper introduces a novel contour-based method for detecting largely affine invariant interest or feature points. In the first step image edges are detected by morphological operators followed by edge thinning. In the second step corner or feature points are identified based on the local curvature of the edges. The main contribution of this work is the selection of good discriminative feature points from the thinned edges based on the 1D empirical mode decomposition EMD . Simulation results compare the proposed method with five existing approaches that yield good results. The suggested contour-based technique detects almost all the true feature points of an image. Repeatability rate which evaluates the geometric stability under different transformations is employed as the performance evaluation criterion. The results show that the performance of the proposed method compares favorably against the existing well-known methods. Copyright 2008 Jesmin Farzana Khan 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 There are a wide variety of methods reported in the literature for interest point and corner detection in greylevel images. .
