| Polynomial Eigenvalue Solutions to Minimal Problems in Computer Vision. | |
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MedLine Citation:
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PMID: 22144525 Owner: NLM Status: Publisher |
Abstract/OtherAbstract:
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We present a method for solving systems of polynomial equations appearing in computer vision. This method is based on polynomial eigenvalue solvers and is more straightforward and easier to implement than the state-of-the-art Gröbner basis method since eigenvalue problems are well studied, easy to understand, and efficient and robust algorithms for solving these problems are available. We provide a characterization of problems that can be efficiently solved as polynomial eigenvalue problems and present a resultant based method for transforming a system of polynomial equations to a polynomial eigenvalue problem. We propose techniques that can be used to reduce the size of the computed polynomial eigenvalue problems. To show the applicability of the proposed polynomial eigenvalue method, we present the polynomial eigenvalue solutions to several important minimal relative pose problems. |
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Authors:
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Zuzana Kukelova; Martin Bujnak; Tomas Pajdla |
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Publication Detail:
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Type: JOURNAL ARTICLE Date: 2011-11-30 |
Journal Detail:
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Title: IEEE transactions on pattern analysis and machine intelligence Volume: - ISSN: 1939-3539 ISO Abbreviation: - Publication Date: 2011 Nov |
Date Detail:
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Created Date: 2011-12-6 Completed Date: - Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 9885960 Medline TA: IEEE Trans Pattern Anal Mach Intell Country: - |
Other Details:
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Languages: ENG Pagination: - Citation Subset: - |
Affiliation:
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Czech Technical University in Prague, Prague. |
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From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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