{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,4,4]],"date-time":"2022-04-04T19:13:02Z","timestamp":1649099582932},"reference-count":14,"publisher":"World Scientific Pub Co Pte Lt","issue":"06","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Patt. Recogn. Artif. Intell."],"published-print":{"date-parts":[[2003,9]]},"abstract":"<jats:p> Orthogonal Fourier\u2013Mellin (OFM) moments have better feature representation capabilities, and are more robust to image noise than the conventional Zernike moments and pseudo-Zernike moments. However, OFM moments have not been extensively used as feature descriptors since they do not possess scale invariance. This paper discusses the drawbacks of the existing methods of extracting OFM moments, and proposes an improved OFM moments. A part of the theory, which proves the improved OFM moments possesses invariance of rotation and scale, is given. The performance of the improved OFM moments is experimentally examined using trademark images, and the invariance of the improved OFM moments is shown to have been greatly improved over the current methods. <\/jats:p>","DOI":"10.1142\/s0218001403002757","type":"journal-article","created":{"date-parts":[[2003,8,29]],"date-time":"2003-08-29T21:24:37Z","timestamp":1062192277000},"page":"983-993","source":"Crossref","is-referenced-by-count":0,"title":["Improvement and Invariance Analysis of Orthogonal  Fourier\u2013Mellin Moments"],"prefix":"10.1142","volume":"17","author":[{"given":"Bin","family":"Ye","sequence":"first","affiliation":[{"name":"Institute for Laser Medicine &amp; Bio-Photonics, College of Life Science &amp; Technology, Shanghai Jiao Tong University,  Shanghai 200030, P.R. China"},{"name":"State Key Lab. for Image  Processing &amp;  Intelligent Control, Institute for Pattern Recognition &amp; Artificial Intelligence, Huazhong University of Science &amp; Technology, Wuhan, Hubei, 430074, P.R. China"}]},{"given":"Jia-Xiong","family":"Peng","sequence":"additional","affiliation":[{"name":"State Key Lab. for Image  Processing &amp;  Intelligent Control, Institute for Pattern Recognition &amp; Artificial Intelligence, Huazhong University of Science &amp; Technology, Wuhan, Hubei, 430074, P.R. China"}]},{"given":"Qiu-Shi","family":"Ren","sequence":"additional","affiliation":[{"name":"Institute for Laser Medicine &amp; Bio-Photonics, College of Life Science &amp; Technology, Shanghai Jiao Tong University,  Shanghai 200030, P.R. China"}]},{"given":"Wan-Rong","family":"Li","sequence":"additional","affiliation":[{"name":"Institute for Laser Medicine &amp; Bio-Photonics, College of Life Science &amp; Technology, Shanghai Jiao Tong University,  Shanghai 200030, P.R. 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