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High Accuracy Fundamental Matrix Computation and Its Performance Evaluation Kenichi KANATANI Yasuyuki SUGAYA Publication IEICE TRANSACTIONS on Information and Systems Vol.E90-D No.2 pp.579-585 Publication Date: 2007/02/01 Online ISSN: 1745-1361 DOI: 10.1093/ietisy/e90-d.2.579 Print ISSN: 0916-8532 Type of Manuscript: PAPER Category: Image Recognition, Computer Vision Keyword: fundamental matrix, geometric fitting, KCR lower bound, maximum likelihood, convergence performance, Full Text: PDF(466.7KB)>>
Summary: We compare the convergence performance of different numerical schemes for computing the fundamental matrix from point correspondences over two images. First, we state the problem and the associated KCR lower bound. Then, we describe the algorithms of three well-known methods: FNS, HEIV, and renormalization. We also introduce Gauss-Newton iterations as a new method for fundamental matrix computation. For initial values, we test random choice, least squares, and Taubin's method. Experiments using simulated and real images reveal different characteristics of each method. Overall, FNS exhibits the best convergence properties. | ||||||||||
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