文章基本信息

标题：Compact Fundamental Matrix Computation
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作者：Kenichi Kanatani ; Yasuyuki Sugaya
期刊名称：Information and Media Technologies
电子版ISSN：1881-0896
出版年度：2010
卷号：5
期号：2
页码：679-690
DOI：10.11185/imt.5.679
出版社：Information and Media Technologies Editorial Board
摘要：A very compact algorithm is presented for fundamental matrix computation from point correspondences over two images. The computation is based on the maximum likelihood (ML) principle, minimizing the reprojection error. The rank constraint is incorporated by the EFNS procedure. Although our algorithm produces the same solution as all existing ML-based methods, it is probably the most practical of all, being small and simple. By numerical experiments, we confirm that our algorithm behaves as expected.