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TP391.4; 为了克服非约束性（光照、表情变化）条件下会大大降低人脸识别率的缺陷，提出一种基于 Fisher判别准则的正则化稀疏表示人脸识别算法。首先将人脸图像经过 Gabor 滤波器滤波得到Gabor幅值图像，提取其统一化的局部二进制直方图，然后利用 Fisher判别准则学习得到新的字典，最后通过正则化的稀疏表示判断测试图像所属类。利用AR数据库的数据进行实验的结果表明，与SRC、FDDL、RSC识别算法相比，本文算法在非约束性条件下具有最佳的识别率。

A good objective metric of image quality assessment （IQA） should be consistent with the subjective judgment of human beings. In this paper, a four-stage perceptual approach for full reference IQA is presented. In the first stage, the visual features are extracted by 2-D Gabor filter that has the excellent performance of modeling the receptive fields of simple cells in the primary visual cortex. Then in the second stage, the extracted features are post-processed by the divisive normalization transform to reflect the nonlinear mechanisms in human visual systems. In the third stage, mutual information between the visual features of the reference and distorted images is employed to measure the visual quality. And in the last pooling stage, the mutual information is converted to the final objective quality score. Experimental results show that the proposed metic has a high correlation with the subjective assessment and outperforms other state-of-the-art metrics.

Automatic recognition of the contents of a scene is an important issue in the field of computer vision. Although considerable progress has been made, the complexity of scenes remains an important challenge to computer vision research. Most previous approaches for scene recognition are based on the so-called ＂bag of visual words＂ model, which uses clustering methods to quantize numerous local region descriptors into a codebook. The size of the codebook and the selection of initial clustering centers greatly affect the performance. Furthermore, the large size of the codebook leads to high computational costs and large memory consumption. To overcome these weaknesses, we present an unsupervised natural scene recognition approach that is not based on the ＂bag of visual words＂ model. This approach constructs multiple images of different resolutions and extracts structural and texturM features from these images. The structural features are represented by weighted histograms of the gradient orientation descriptor, which is presented in this paper, and the textural features are represented by filter responses of Gabor filters and a Schmid set. We regard the structural and textural features as two independent feature channels, and combine them to realize automatic categorization of scenes using a support vector machine. We then evaluated our approach using three commonly used datasets with various scene categories. Our experiments demonstrate that the weighted histograms of the gradient orientation descriptor outperform the classical scMe invariant feature transform descriptor in natural-scene recognition, and our approach achieves good performance with respect to current state-of-the-art methods.

In this paper we propose an optimized seam-carving approach for anti-shearing image resizing.Current image/video seam-carving strategy only focuses on deleting the pixels along 8-connected seams,which may lead to the obvious information loss during resizing.Unlike the traditional seam-carving methods,our approach can construct a seamlet to handle the discontinuous pixels and further achieve an optimized image resizing results with shape-preservation.In particular,to suppress zigzag artifacts,we introduce the anti-shearing energy into the seamlet generation,which includes two steps：1） the energy map is formalized by using Gabor filtering and the saliency map;2） The energy computation is optimized in Gabor feature space to obtain the resizing results without the connectivity restriction.The experimental results and comparisons show the effectiveness of our method.

An innovative and uniform framework based on a combination of Gabor wavelets with principal component analysis （PCA） and multiple discriminant analysis （MDA） is presented in this paper. In this framework, features are extracted from the optimal random image components using greedy approach. These feature vectors are then projected to subspaces for dimensionality reduction which is used for solving linear problems. The design of Gabor filters, PCA and MDA are crucial processes used for facial feature extraction. The FERET, ORL and YALE face databases are used to generate the results. Experiments show that optimal random image component selection （ORICS） plus MDA outperforms ORICS and subspace projection approach such as ORICS plus PCA. Our method achieves 96.25%, 99.44% and 100% recognition accuracy on the FERET, ORL and YALE databases for 30% training respectively. This is a considerably improved performance compared with other standard methodologies described in the literature.