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Revisiting Hartley's normalized eight-point algorithm

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dc.contributor.authorChojnacki, W.
dc.contributor.authorBrooks, M.
dc.contributor.authorVan Den Hengel, A.
dc.contributor.authorGawley, D.
dc.date.issued2003
dc.descriptionCopyright © 2003 IEEE
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.
dc.description.statementofresponsibilityWojciech Chojnacki, Michael J. Brooks, Anton van den Hengel and Darren Gawley
dc.identifier.citationIEEE Transactions on Pattern Analysis and Machine Intelligence, 2003; 25(9):1172-1177
dc.identifier.doi10.1109/TPAMI.2003.1227992
dc.identifier.issn0162-8828
dc.identifier.issn1939-3539
dc.identifier.orcidChojnacki, W. [0000-0001-7782-1956]
dc.identifier.orcidBrooks, M. [0000-0001-9612-5884]
dc.identifier.orcidVan Den Hengel, A. [0000-0003-3027-8364]
dc.identifier.urihttp://hdl.handle.net/2440/1328
dc.language.isoen
dc.publisherIEEE Computer Soc
dc.relation.grantARC
dc.source.urihttps://doi.org/10.1109/tpami.2003.1227992
dc.subjectEpipolar equation
dc.subjectfundamental matrix
dc.subjecteight-point algorithm
dc.subjectdata normalization
dc.titleRevisiting Hartley's normalized eight-point algorithm
dc.typeJournal article
pubs.publication-statusPublished

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