Current Issue : July - September Volume : 2013 Issue Number : 3 Articles : 4 Articles
Despite their high stability and compactness, chord-length shape features have received relatively little attention in the human\r\naction recognition literature. In this paper, we present a new approach for human activity recognition, based on chord-length\r\nshape features. The most interesting contribution of this paper is twofold.We first show how a compact, computationally efficient\r\nshape descriptor; the chord-length shape features are constructed using 1-D chord-length functions. Second, we unfold how to\r\nuse fuzzy membership functions to partition action snippets into a number of temporal states. On two benchmark action datasets\r\n(KTH and WEIZMANN), the approach yields promising results that compare favorably with those previously reported in the\r\nliterature, while maintaining real-time performance....
Data structures such as k-D trees and hierarchical k-means trees perform very well in approximate k nearest neighbour matching,\r\nbut are only marginally more effective than linear search when performing exact matching in high-dimensional image descriptor\r\ndata. This paper presents several improvements to linear search that allows it to outperform existing methods and recommends\r\ntwo approaches to exact matching. The first method reduces the number of operations by evaluating the distance measure in order\r\nof significance of the query dimensions and terminating when the partial distance exceeds the search threshold. This method does\r\nnot require preprocessing and significantly outperforms existing methods. The second method improves query speed further by\r\npresorting the data using a data structure called d-D sort. The order information is used as a priority queue to reduce the time\r\ntaken to find the exact match and to restrict the range of data searched. Construction of the d-D sort structure is very simple to\r\nimplement, does not require any parameter tuning, and requires significantly less time than the best-performing tree structure, and\r\ndata can be added to the structure relatively efficiently....
Grayscale and color textures can have spectral informative content. This spectral information coexists with the grayscale or\r\nchromatic spatial pattern that characterizes the texture. This informative and nontextural spectral content can be a source of\r\nconfusion for rigorous evaluations of the intrinsic textural performance of texture methods. In this paper, we used basic image\r\nprocessing tools to develop a new class of textures in which texture information is the only source of discrimination. Spectral\r\ninformation in this new class of textures contributes only to form texture. The textures are grouped into two databases. The first\r\nis the Normalized Brodatz Texture database (NBT) which is a collection of grayscale images. The second is the Multiband Texture\r\n(MBT) database which is a collection of color texture images. Thus, this new class of textures is ideal for rigorous comparisons\r\nbetween texture analysis methods based only on their intrinsic performance on texture characterization...
Recognizing avatar faces is a very important issue for the security of virtual worlds. In this paper, a novel face recognition technique\r\nbased on the wavelet transform and the multiscale representation of the adaptive local binary pattern (ALBP) with directional\r\nstatistical features is proposed to increase the accuracy rate of recognizing avatars in different virtual worlds. The proposed\r\ntechnique consists of three stages: preprocessing, feature extraction, and recognition. In the preprocessing and feature extraction\r\nstages, wavelet decomposition is used to enhance the common features of the same subject of images and the multiscale ALBP\r\n(MALBP) is used to extract representative features from each facial image. Then, in the recognition stage the wavelet MALBP\r\n(WMALBP) histogram dissimilarity with statistical features of each test image and each class model is used within the nearest\r\nneighbor classifier to improve the classification accuracy of the WMALBP. Experiments conducted on two virtual world avatar\r\nface image datasets show that our technique performs better than LBP, PCA, multiscale local binary pattern, ALBP, and ALBP with\r\ndirectional statistical features (ALBPF) in terms of the accuracy and the time required to classify each facial image to its subject....
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