Progress in Pattern Recognition, Image Analysis and Applications

Progress in Pattern Recognition, Image Analysis and Applications

4.11 - 1251 ratings - Source

The ongoing success of the Iberoamerican Congress on Pattern Recognition (CIARP) re?ects the growing need for developing new theory and applications of pattern recognition, which is being confronted by many reseachers. The 11th Iberoamerican Congress on Pattern Recognition (CIARP 2006) was the 11th event in the premier series of research agenda-de?ning conferences on pattern recognition in the Iberoamerican community. As in the previous years, CIARP 2006 attracted worldwide participation.The aim of the congresswas to promote and disseminate ongoing research and mathematical methods for pattern rec- nition, imageanalysis, andapplicationsinsuchdiverseareasascomputer vision, robotics and remote sensing, industry, health, space exploration, data mining, document analysis, naturallanguageprocessingandspeechrecognition, to name afew. CIARP 2006, held in Cancun, Mexico, was organized by the Computer Science Department of the National Institute of Astrophysics, Optics and El- tronics (INAOE). The event was sponsored by the Advanced Technologies - plication Center of Cuba (CENATAV), the Mexican Association for Computer Vision, Neurocomputing and Robotics (MACVNR), the Cuban Association for Pattern Recognition (ACRP), the Portuguese Association for Pattern Recog- tion(APRP), theSpanishAssociationforPatternRecognitionandImageAna- sis(AERFAI), theSpecialInterestGrouponPatternRecognitionoftheBrazilian ComputerSociety(SIGPR-SBC), andtheChileanAssociationforPatternRec- nition(ACHRP).CIARP2006wasendorsedbytheInternationalAssociationfor PatternRecognition(IAPR). Contributions were received from 36 countries. In total 239 papers were s- mitted, out of which 99 were accepted for publication in these proceedings and for presentation at the conference. The review process was carried out by the Scienti?c Committee, composed of internationally recognized scientists, all - perts in their respective ?elds. We are indebted to them for their e?orts and the quality of the reviews.Color Texture Segmentation by Decomposition of Gaussian Mixture Model JiE‡r IAp Grim, Petr Somol, Michal Haindl, and Pavel Pudil Institute of ... http://ro Abstract. ... In this paper the mixture model is decomposed by maximizing the mean probability of correct classification of pixels into segments in a way taking into account the assumed consistency of final segmentation.

Title:Progress in Pattern Recognition, Image Analysis and Applications
Author:José Francisco Martínez-Trinidad, Jesús Ariel Carrasco Ochoa, Josef Kittler
Publisher:Springer Science & Business Media - 2006-10-12


You Must CONTINUE and create a free account to access unlimited downloads & streaming