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This paper proposes a 3D model retrieval system based on three-views hand-drawn sketches. In the system, users can express their desired 3D models by drawing three sketches from front, side and top viewing directions, and the system can automatically returns a set of 3D models whose 2D views are similar to the input sketches. Fourier Descriptors and HOG are integrated as features which are used to...
In this paper, author has introduced the standard and content of AEKD, analyzed the function of building AEKD's manage system, AEKD Web site management system and the progress of AEKD.
Storage class memory (SCM), a new generation of memory technology, offers non-volatility, high-speed, and byte-addressability, which combines the best properties of current hard disk drives (HDD) and main memory. With these extraordinary features, current systems and software stacks need to be redesigned to get significantly improved performance by eliminating disk input/output (I/O) barriers; and...
In Two-Dimensional Linear Discriminant Analysis (2DLDA), it is satisfied that within-class covariance matrixes are equal; while in Two-Dimensional Heteroscedastic Discriminant Analysis (2DHDA), within-class covariance matrixes are heteroscedastic. Based on the characters of 2DLDA and 2DHDA, Weighted Two-Dimensional Heteroscedastic Discriminant Analysis (W2DHDA) is introduced and used in face recognition,...
Kernel method is a nonlinear feature extraction approach. Firstly, the samples in the original feature space are transformed into a higher dimensional feature space by nonlinear mapping. Then, linear approaches are used in the higher dimensional feature space, and thus nonlinear features of original samples are extracted. The Heteroscedastic Discriminant Analysis (HDA), in which the equal within-class...
Kernel method is a nonlinear feature extraction approach. Firstly, the samples in the original feature space are transformed into a higher dimensional feature space by nonlinear mapping. Then, linear approaches are used in the higher dimensional feature space, and thus nonlinear features of original samples are extracted. The Heteroscedastic Discriminant Analysis (HDA), in which the equal within-class...
In this paper, a novel discriminant analysis named two-dimensional Heteroscedastic Discriminant Analysis (2DHDA) is presented for face recognition. In 2DHDA, small sample size problem (S3 problem) of Heteroscedastic Discriminant Analysis (HAD) is overcome. Firstly, the criterion of 2DHDA is defined according to that of 2DLDA. Secondly, criterion of 2DHDA, log and rearranging terms are taken, and then...
On the basis of two dimensional principal component analysis, an improved two dimensional principal component analysis (I2DPCA) is presented for face recognition. Firstly, the criterion functions of global and between class scatters of projection features are defined. Secondly, the two defined criterion functions are fused by way of multiplication or addition. Therefore, the criterion function of...
Face recognition has been of interest to a growing number of researchers, and many algorithms are presented. However, the recognition rate will be significantly reduced in the case of large sample size and greater facial expression changes. In this paper, 2DPCA algorithm is used for features extraction and Boosting by filtering method is used to choose training samples. Then, the expert systems of...
This paper presents a novel algorithm-kernel based 2D symmetrical principal component analysis (K2DSPCA), which takes full advantage of kernel method, the symmetrical property of facial image and the structural information of image (i.e., the advantage of two-dimensional PCA). Firstly, a facial image is decomposed into an even image and an odd image; Secondly, both the even image and the odd image...
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