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<identifier>oai:HAL:hal-00602265v1</identifier>
<datestamp>2018-01-11</datestamp>
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<publisher>HAL CCSD</publisher>
<title lang=fr>Segmentation et détection d'objets par caractérisation multi-échelle</title>
<creator>Grandchamp, Enguerran</creator>
<creator>Philippe, Marthon</creator>
<contributor>Laboratoire de Mathématiques Informatique et Applications (LAMIA) ; Université des Antilles et de la Guyane (UAG)</contributor>
<contributor>Institut de recherche en informatique de Toulouse (IRIT) ; Institut National Polytechnique [Toulouse] (INP) - Université Toulouse 1 Capitole (UT1) - Université Toulouse 2 (UT2) - Université Paul Sabatier - Toulouse 3 (UPS) - Centre National de la Recherche Scientifique (CNRS)</contributor>
<description>International audience</description>
<source>RFIA Proceedings</source>
<source>RFIA</source>
<coverage>Toulouse, France</coverage>
<identifier>hal-00602265</identifier>
<identifier>https://hal.archives-ouvertes.fr/hal-00602265</identifier>
<source>https://hal.archives-ouvertes.fr/hal-00602265</source>
<source>RFIA, Jan 2004, Toulouse, France. pp.1, 2004</source>
<language>fr</language>
<subject lang=fr>traitement d'images</subject>
<subject lang=fr>exposants de Lipschitz</subject>
<subject lang=fr>reconnaissance de formes</subject>
<subject lang=fr>ondelettes</subject>
<subject lang=fr>segmentation</subject>
<subject lang=fr>classification</subject>
<subject>[INFO.INFO-TI] Computer Science [cs]/Image Processing</subject>
<type>info:eu-repo/semantics/conferenceObject</type>
<type>Conference papers</type>
<description lang=en>Half away between edge detection and object recognition this study aim to characterize singularities in order to guide this two phases. The caracterization is obtained with a generalization of the Lipschitz exponents .for complex structures. This new characterization is called " maxima chain " and integrates the notion of value and spatial repartition of the maxima. The maxima chains are obtained with a multiscale decomposition using a Diadic Discrete Wavelet Transform (DDWT). We use the maxima of the gradient image computed with the wavelet details. The local maxima detect the position of irregular structures (edges) in the image (Canny algorithm). Then we characterize each maxima using their evolution across the different scales. A classification is made using the maxima chains. We integrate the classification to eliminate unwanted maxima (from noise or other objects) to drive the search of closed contours. In fact, there is a totally different signature from one maxima to another depending on the parameters of the objects : nature, shape, size, gray scale, texture, ...</description>
<date>2004-01-28</date>
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