High Accuracy Fundamental Matrix Computation and Its Performance Evaluation(Image Recognition, Computer Vision)
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概要
- 論文の詳細を見る
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.
- 社団法人電子情報通信学会の論文
- 2007-02-01
著者
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KANATANI Kenichi
Department of Computer Science, Okayama University
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Sugaya Yasuyuki
Department Of Information And Computer Sciences Toyohashi University Of Technology
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Sugaya Yasuyuki
Department Of Computer Science And Engineering Toyohashi University Of Technology
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Kanatani Kenichi
Department Of Computer Science Okayama University
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Kanatani Kenichi
Department Of Computer Science Gunma University
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