Earticle

This study presents a tri-level thresholding method for image segmentation with invasive weed optimization (IWO) algorithm. The objective of the proposed approach is to handle the nonextensivity and vagueness of image in segmentation, in the meanwhile to reduce the computation time. In this study, the histogram of image is converted to fuzzy domain by membership function firstly. Then the thresholding method is constructed through maximizing the sum of nonextensive entropy of subsets of the each part of fuzzy histogram. The IWO algorithm is used to search the optimal thresholds to reduce the computation time in the new method. Experiments on synthetic and real-world images are given to demonstrate the effectiveness of the proposed approach compared with the other methods.

In this paper, based on the study of the Two-Dimensional Principal Component Analysis (2DPCA), Two-Dimensional Principal Component Analysis (2DPCA) and fuzzy set theory, we propose a integrated face recognition algorithm based on wavelet subspace. This method can make good use of the advantages of each single method, and also can make up for the defect of each other. The comparison of the results of the different methods identification effect on the ORL、YALE and FERET face database show, the integrated method proposed in this paper improves the recognition rate, and it also reduces the training and classification time as well.

Based on the problems that target appears rotation and noise interference in complex environment, an improved multi-feature adaptive fusion tracking method is proposed. The algorithm adopts unscented Kalman particle filter (UPF) to update the measurement information in the sample particles, better overcome the problem of the particle weight degradation. In addition, in order to overcome the defects of additive and multiplicative fusion algorithm in the feature selection, the multiple adaptive fusion characteristics method that target color distribution and scale invariance feature (SIFT) are used as complementary information. Experimental results show that the proposed method is superior to the traditional methods which are based on fixed weight or standard particle filter.