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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 Perceptual Image Representation | Hindawi Publishing Corporation EURASIP Journal on Image and Video Processing Volume 2007 Article ID98181 9 pages doi 10.1155 2007 98181 Research Article Perceptual Image Representation Matei Mancas 1 Bernard Gosselin 1 and Benoit Macq2 1 Theorie des Circuits et Traitement du Signal TCTS Lab Faculte Polytechnique de Mons 7000 Mons Belgium 2 Laboratoire de Telecommunications et Teledetection TELE Universite Catholique de Louvain 1348 Louvain-la-Neuve Belgium Received 1 August 2006 Revised 8 March 2007 Accepted 2 July 2007 Recommended by Ling Guan This paper describes a rarity-based visual attention model working on both still images and video sequences. Applications of this kind of models are numerous and we focus on a perceptual image representation which enhances the perceptually important areas and uses lower resolution for perceptually less important regions. Our aim is to provide an approximation of human perception by visualizing its gradual discovery of the visual environment. Comparisons with classical methods for visual attention show that the proposed algorithm is well adapted to anisotropic filtering purposes. Moreover it has a high ability to preserve perceptually important areas as defects or abnormalities from an important loss of information. High accuracy on low-contrast defects and scalable real-time video compression may be some practical applications of the proposed image representation. Copyright 2007 Matei Mancas et al. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. 1. INTRODUCTION The human visual system HVS is a topic of increasing importance in computer vision research since Hubel s work 1 and the comprehension of the basics of biological vision. Mimicking some of the processes done by our visual system may help to improve the current computer vision systems. Visual attention is .
