tailieunhanh - Báo cáo hóa học: Research Article Hybrid Modeling of Intra-DCT Coefficients for Real-Time Video Encoding

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 Hybrid Modeling of Intra-DCT Coefficients for Real-Time Video Encoding | Hindawi Publishing Corporation EURASIP Journal on Image and Video Processing Volume 2008 Article ID 743103 20 pages doi 2008 743103 Research Article Optimal Features Subset Selection and Classification for Iris Recognition Kaushik Roy and Prabir Bhattacharya Concordia Institute for Information Systems Engineering Concordia University Montreal Quebec Canada H3G 1M8 Correspondence should be addressed to Kaushik Roy kaush_ro@ Received 1 August 2007 Revised 19 November 2007 Accepted 11 March 2008 Recommended by Kenneth M. Lam The selection of the optimal features subset and the classification have become an important issue in the field of iris recognition. We propose a feature selection scheme based on the multiobjectives genetic algorithm MOGA to improve the recognition accuracy and asymmetrical support vector machine for the classification of iris patterns. We also suggest a segmentation scheme based on the collarette area localization. The deterministic feature sequence is extracted from the iris images using the 1D log-Gabor wavelet technique and the extracted feature sequence is used to train the support vector machine SVM . The MOGA is applied to optimize the features sequence and to increase the overall performance based on the matching accuracy of the SVM. The parameters of SVM are optimized to improve the overall generalization performance and the traditional SVM is modified to an asymmetrical SVM to treat the false accept and false reject cases differently and to handle the unbalanced data of a specific class with respect to the other classes. Our experimental results indicate that the performance of SVM as a classifier is better than the performance of the classifiers based on the feedforward neural network the k-nearest neighbor and the Hamming and the Mahalanobis distances. The proposed technique is computationally effective with recognition rates of and on CASIA and ICE datasets respectively. Copyright 2008 K. Roy and