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dc.contributor.authorChojnacki, W.en
dc.contributor.authorBrooks, M.en
dc.contributor.authorVan Den Hengel, A.en
dc.contributor.authorGawley, D.en
dc.identifier.citationIEEE Transactions on Pattern Analysis and Machine Intelligence, 2003; 25(9):1172-1177en
dc.descriptionCopyright © 2003 IEEEen
dc.description.abstractHartley's eight-point algorithm has maintained an important place in computer vision, notably as a means of providing an initial value of the fundamental matrix for use in iterative estimation methods. In this paper, a novel explanation is given for the improvement in performance of the eight-point algorithm that results from using normalized data. It is first established that the normalized algorithm acts to minimize a specific cost function. It is then shown that this cost function I!; statistically better founded than the cost function associated with the nonnormalized algorithm. This augments the original argument that improved performance is due to the better conditioning of a pivotal matrix. Experimental results are given that support the adopted approach. This work continues a wider effort to place a variety of estimation techniques within a coherent framework.en
dc.description.statementofresponsibilityWojciech Chojnacki, Michael J. Brooks, Anton van den Hengel and Darren Gawleyen
dc.publisherIEEE Computer Socen
dc.subjectEpipolar equation; fundamental matrix; eight-point algorithm; data normalizationen
dc.titleRevisiting Hartley's normalized eight-point algorithmen
dc.typeJournal articleen
pubs.library.collectionComputer Science publicationsen
dc.identifier.orcidChojnacki, W. [0000-0001-7782-1956]en
dc.identifier.orcidVan Den Hengel, A. [0000-0003-3027-8364]en
Appears in Collections:Computer Science publications

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