The Multiple Signal Classification (MUSIC) method is a typical method for high-resolution Direction Of Arrival(DOA) estimation. Usually it performs spectrum search in certain grid space, which inevitably leads to high...
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The Multiple Signal Classification (MUSIC) method is a typical method for high-resolution Direction Of Arrival(DOA) and frequency estimation. Usually it performs spectrum search in certain grid space, which inevitably...
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The optimal kernel selection is a critical problem for the kernel-based learning algorithm. In order to obtain good results, the kernel function must be chosen in a data-dependent manner. To this end, we propose a new...
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Foreground object extraction, which aims to accurately separate a foreground object from its background in still images, plays an important role in many computer vision applications. An interactive object extraction m...
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As we all know, the content-based image retrieval (CBIR) is very time-consuming due to the extraction and matching of high dimensional and complex features. The traditional CBIR systems could not respond to a very lar...
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In this paper we propose a novel framework for action recognition based on multiple features for improve action recognition in videos. The fusion of multiple features is important for recognizing actions as often a si...
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A distributed warehouse management system is design based on the *** framework. Unlike many frameworks which focus on the database data operation or construct flexible user interface, the proposed project mainly focus...
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Based on the discrete Fourier transformation (DFT) and Hough transforms, a novel digital watermarking method is proposed. The experiment results show that the algorithm is more robust than the traditional watermark al...
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In order to protect the copyright of the image, in this paper proposed a novel important sub-tree (Istree) digital watermarking algorithm based on contourlet transform. First, Shuffling is applied by watermarking imag...
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A new algorithm, Laplacian MinMax Discriminant Projection (LMMDP), is proposed in this paper for supervised dimensionality reduction. LMMDP aims at learning a discriminant linear transformation. Specifically, we defin...
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