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dc.contributor.authorBotte-Lecocq, C.en_US
dc.contributor.authorHammouche, K.en_US
dc.contributor.authorMoussa, A.en_US
dc.date.accessioned2016-08-02T08:13:33Z
dc.date.available2016-08-02T08:13:33Z
dc.date.issued2007en_US
dc.identifier.isbn978-3-902613-06-6en_US
dc.identifier.otherHPU3160481en_US
dc.identifier.urihttps://lib.hpu.edu.vn/handle/123456789/22709
dc.description.abstractAll the clustering methods presented in this chapter tend to generalize bi-dimensional procedures initially developed for image processing purpose. Among them, thresholding, edge detection, probabilistic relaxation, mathematical morphology, texture analysis, and Markov field models appear to be valuable tools with a wide range of applications in the field of unsupervised pattern classification. Following the same idea of adapting image processing techniques to cluster analysis, one of our other objectives is to model spatial relationships between pixels by means of other textural parameters derived from autoregressive models (Comer & Delp, 1999), Markov random fields models (Cross & Jain, 1983), Gabor filters (Jain & Farrokhnia, 1991), wavelet coefficients (Porter & Canagarajah, 1996) and fractal geometry (Keller & Crownover, 1989)en_US
dc.format.extent23 p.en_US
dc.format.mimetypeapplication/pdf
dc.language.isoenen_US
dc.publisherINTECH Open Access Publisheren_US
dc.subjectScene Reconstruction Pose Estimation and Trackingen_US
dc.titleImage Processing Techniques for Unsupervised Pattern Classificationen_US
dc.typeBooken_US
dc.size446KBen_US
dc.departmentEducationen_US


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