Entropy And Vision

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dc.creatorKanhouche, Rami
dc.date2006-06-26
dc.date2006-07-18
dc.date.accessioned2026-07-07T07:17:41Z
dc.date.available2026-07-07T07:17:41Z
dc.descriptionIn vector quantization the number of vectors used to construct the codebook is always an undefined problem, there is always a compromise between the number of vectors and the quantity of information lost during the compression. In this text we present a minimum of Entropy principle that gives solution to this compromise and represents an Entropy point of view of signal compression in general. Also we present a new adaptive Object Quantization technique that is the same for the compression and the perception.
dc.identifierhttps://arxiv.org/abs/math/0606643
dc.identifierhttp://arxiv.org/abs/math/0606643
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/114029
dc.subjectProbability
dc.subjectComputer Vision and Pattern Recognition
dc.subjectDatabases
dc.subjectDiscrete Mathematics
dc.subjectMachine Learning
dc.subjectCombinatorics
dc.subjectI.2.10 Vision and Scene Understanding
dc.titleEntropy And Vision
dc.typetext

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