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dc.contributor.authorMohamed Rizon-
dc.contributor.authorHaniza, Yazid-
dc.contributor.authorPuteh, Saad-
dc.contributor.authorAli Yeon, Md Shakaff, Prof. Dr.-
dc.contributor.authorAbdul Rahman, Saad-
dc.contributor.authorMohd Rozailan, Mamat-
dc.contributor.authorSazali, Yaacob, Prof. Dr.-
dc.contributor.authorHazri, Desa, Dr.-
dc.contributor.authorKarthigayan, M.-
dc.date.accessioned2011-03-21T09:18:14Z-
dc.date.available2011-03-21T09:18:14Z-
dc.date.issued2006-
dc.identifier.citationAmerican Journal of Applied Sciences, vol. 3(6), 2006, pages 1876-1878en_US
dc.identifier.issn1546-9239-
dc.identifier.urihttp://www.scipub.org/fulltext/ajas/ajas361876-1878.pdf-
dc.identifier.urihttp://dspace.unimap.edu.my/123456789/11347-
dc.descriptionLink to publisher's homepage at http://www.scipub.org/en_US
dc.description.abstractGeometric moment invariant produces a set of feature vectors that are invariant under shifting, scaling and rotation. The technique is widely used to extract the global features for pattern recognition due to its discrimination power and robustness. In this paper, moment invariant is used to identify the object from the captured image using the first invariant (Ø1). The recognition rate for this technique is 90% after the image undergoes suitable processing and segmentation process.en_US
dc.language.isoenen_US
dc.publisherScience Publicationsen_US
dc.subjectGeometric invariant momenten_US
dc.subjectObject detectionen_US
dc.subjectGeometric Moment (GM)en_US
dc.titleObject detection using geometric invariant momenten_US
dc.typeArticleen_US
Appears in Collections:School of Computer and Communication Engineering (Articles)
Sazali Yaacob, Prof. Dr.
Ali Yeon Md Shakaff, Dato' Prof. Dr.
Haniza Yazid, Dr.

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