{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,6,22]],"date-time":"2024-06-22T05:17:26Z","timestamp":1719033446197},"reference-count":22,"publisher":"World Scientific Pub Co Pte Lt","issue":"04","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Patt. Recogn. Artif. Intell."],"published-print":{"date-parts":[[2005,6]]},"abstract":"<jats:p> This paper presents a method for automatic determination of dense and smooth mapping between two images without a priori knowledge of either the camera pose or the objects in the images. We designed an algorithm to find the mapping between a pair of arbitrary images, and accomplish automatic image morphing. In order to extract image features which look natural to human, we use a set of linear filters similar to those that are used in early vision. Then the derived vector fields consisting of filter responses are matched with each other through a minimization of the cost function which expresses the similarity of transformed images and mapping smoothness, in a multiresolutional hierarchy. Since the cost function in general is highly nonlinear, we avoid excessive distortion in the estimated mapping by providing a local convexity of mapping in nonlinear optimization. In this paper, a variety of experimental results are discussed for various data sets, including images of rotating objects, static objects, human faces and texture patterns, to demonstrate the performance of the proposed method. <\/jats:p>","DOI":"10.1142\/s0218001405004162","type":"journal-article","created":{"date-parts":[[2005,7,6]],"date-time":"2005-07-06T22:21:56Z","timestamp":1120688516000},"page":"565-583","source":"Crossref","is-referenced-by-count":1,"title":["PLAUSIBLE IMAGE MATCHING: DETERMINING DENSE AND SMOOTH MAPPING BETWEEN IMAGES WITHOUT <i>A PRIORI<\/i> KNOWLEDGE"],"prefix":"10.1142","volume":"19","author":[{"given":"SHUNTARO","family":"YAMAZAKI","sequence":"first","affiliation":[{"name":"Digital Human Research Center, National Institute of Advanced Industrial Science and Technology (AIST), Water Front 3F, 2-41-6, Aomi, Koto-ku, Tokyo 135-0064, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"KATSUSHI","family":"IKEUCHI","sequence":"additional","affiliation":[{"name":"Industrial Institute of Science, University of Tokyo, 4-6-1 Komaba, Meguro, Tokyo 153-8505, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"YOSHIHISA","family":"SHINAGAWA","sequence":"additional","affiliation":[{"name":"Beckman Institute, University of Illinois, 2021 Beckman Institute, University of Illinois, 405 N. 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