Computer vision and pattern recognition

This Special Issue of International Journal of Computer Mathematics (IJCM) offers a venue to present innovative approaches in computer vision and pattern recognition, which have been changing our everyday life dramatically over the last few years. Below we present the origin of this Special Issue and then give an overview of the papers included. The papers emanate from the International Workshop on Intelligent Computing in Pattern Analysis/Synthesis (IWICPAS 2006), which ran as a satellite workshop of the 18th International Conference on Pattern Recognition. The former took place in Xi’an on 26–27 August 2006 and the latter in Hong Kong on 20–24 August 2006, both in P. R. China. IWICPAS brought together people from academia and industry in the fields of computer vision and pattern recognition, and most of the pattern recognition algorithms presented have been employed on vision-related applications. The IWICPAS Workshop attracted a record number of 264 paper submissions from 20 countries. From these 264 papers, 51 were accepted. The proceedings were published as a book in the LNCS (Lecture Notes in Computer Science) series of Springer-Verlag. Thereafter, out of these 51 papers, nine were selected for this Special Issue. These were then substantially extended and went through another round of reviewing. We strongly believe that it is now an ideal time to publish this Special Issue with the aforesaid selected papers. The contents will provide readers of IJCM with cutting-edge and topical information for their related research. As we look forward to the future, we envisage computer vision and pattern recognition research to significantly benefit from cognitive science and psychology, as evidenced in the paper by Oyekoya and Stentiford. This Special Issue starts with three papers on feature generation and selection. The next two papers focus on the classical segmentation problem, which is a basic task in various image processing systems. After three compression and coding related papers, the last paper deals with the interaction between the human vision system and a computerized visual information management system. We now highlight the core contributions of each of the papers. In the paper ‘BTF modelling using BRDF texels’, Filip and Haindl developed two schemes to enlarge Bidirectional Texture Function (BTF) data. The two schemes perform best for spatially random BTF. In their paper ‘Combinatorial and statistical methods for part selection for object recognition’, Zhao et al. presented a statistical method for choosing image patches and then using these patches for object detection and classification. Chen and Yang, in their

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