tailieunhanh - Báo cáo hóa học: " Research Article Comparative Study of Contour Detection Evaluation Criteria Based on Dissimilarity Measures"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Comparative Study of Contour Detection Evaluation Criteria Based on Dissimilarity Measures | Hindawi Publishing Corporation EURASIP Journal on Image and Video Processing Volume 2008 Article ID 693053 13 pages doi 2008 693053 Research Article Comparative Study of Contour Detection Evaluation Criteria Based on Dissimilarity Measures Sébastien Chabrier 1 Helene Laurent 2 Christophe Rosenberger 3 and Bruno Emile2 1 Laboratoire Terre-OcỀan Universite de la Polynesie Francaise BP 6570 98702 Faa a Tahiti Polynésie Pranẹaise France 2 Institut PRISME ENSI de Bourges Universite d Orleans 88 boulevard Lahitolle 18020 Bourges Cedex France 3 Laboratoire GREYC ENSICAEN Universite de Caen CNRS 6 boulevard du Marechal Juin 14050 Caen Cedex France Correspondence should be addressed to Helene Laurent Received 18 July 2007 Revised 5 November 2007 Accepted 7 January 2008 Recommended by Ferran Marques We present in this article a comparative study of well-known supervised evaluation criteria that enable the quantification of the quality of contour detection algorithms. The tested criteria are often used or combined in the literature to create new ones. Though these criteria are classical ones none comparison has been made on a large amount of data to understand their relative behaviors. The objective of this article is to overcome this lack using large test databases both in a synthetic and a real context allowing a comparison in various situations and application fields and consequently to start a general comparison which could be extended by any person interested in this topic. After a review of the most common criteria used for the quantification of the quality of contour detection algorithms their respective performances are presented using synthetic segmentation results in order to show their performance relevance face to undersegmentation oversegmentation or situations combining these two perturbations. These criteria are then tested on natural images in order to process the diversity of the possible encountered situations. The used

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