01915oam 2200433zu 450 991014669380332120241212215520.097815090883311509088334(CKB)1000000000524971(SSID)ssj0001034838(PQKBManifestationID)12363782(PQKBTitleCode)TC0001034838(PQKBWorkID)11016669(PQKB)11202547(NjHacI)991000000000524971(EXLCZ)99100000000052497120160829d2007 uy engur|||||||||||txtccr2007 IEEE 11th International Conference on Computer Vision[Place of publication not identified]IEEE20071 online resourceBibliographic Level Mode of Issuance: Monograph9781424416301 1424416302 We propose a simple probabilistic generative model for image segmentation. Like other probabilistic algorithms (such as EM on a Mixture of Gaussians) the proposed model is principled, provides both hard and probabilistic cluster assignments, as well as the ability to naturally incorporate prior knowledge. While previous probabilistic approaches are restricted to parametric models of clusters (e.g., Gaussians) we eliminate this limitation. The suggested approach does not make heavy assumptions on the shape of the clusters and can thus handle complex structures. Our experiments show that the suggested approach outperforms previous work on a variety of image segmentation tasks.Computer visionCongressesComputer vision006.37IEEE StaffPQKBPROCEEDING99101466938033212007 IEEE 11th International Conference on Computer Vision2406320UNINA