In this paper, we define the intersection matrix to represent the spatial relations of two concave regions. An algorithm is given to show that there are at most 161 possible topological relations between two concave r...
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In this article, on the basis of RCC-8, the 9-intersection matrix is extended to 27-intersection matrix, to represent the topological relations between a region with broad boundaries and a simple region. We get 23 top...
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The multilevel thresholding problem is a challenge task due to the fact that the computation is usually very time-consuming for obtaining the optimal multilevel thresholds. Though the state-of-the-art multilevel thres...
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In order to improve the detection accuracy of spliced images, a new blind detection based on visual saliency was proposed in this paper. Firstly, create the edge conspicuous map by an improved OSF-based method, and ex...
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This paper focuses on a new color-gray image fusion algorithm based on Morphological Component Analysis (MCA) which is a novel decomposition (separation) method based on sparse representation of signals and images. Th...
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With the development of high-throughput microarray technology, a large number of microarray data has been obtained by tens of thousands of simulation experiments on gene expression. However, due to the high cost, gene...
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In this paper, we propose a non-interior path following algorithm for solving the optimal power flow problem. By using the new approach, we can choose initial guesses arbitrarily in the whole space of n dimensional Eu...
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A new image resizing algorithm was proposed based on a color space and an energy updating mechanism. First, the concept of color space was introduced into image resizing algorithm and the pixel's energy function w...
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A new image resizing algorithm was proposed based on a color space and an energy updating mechanism. First, the concept of color space was introduced into image resizing algorithm and the pixel's energy function was re-built based on the gradient of gray space and color space. Three kinds of color energy functions were tested and the energy function in HSI space was finally chosen because of its excellent performance. Additionally, since image resizing might badly corrupt the contours of main areas in an original image and might further affect the understanding of the image, an energy updating mechanism was proposed. The energy updating mechanism could increase the neighborhood energy values where a pixel had been deleted. By doing so, it could decrease the probability that seams cross this area again, thus the contour of this area could be protected well. In experiments, the degree of Scale Invariant Feature Transform (SIFT) matching between original image and resized image with different λ values was calculated. The best performance shows that the proportion between the color and gray energy functions was 7:3. By comparing the proposed method with other two state of arts algorithms, it indicates that the shape of main area will be better preserved with the proposed algorithm without introducing any more complexity.
Simple Tabular Reduction algorithms (STR) work well to establish Generalized Arc Consistency (GAC) on positive table constraints. However, the existing STR algorithms are useless for negative table constraints. In thi...
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We describe a novel mobile visual search system based on the saliency mechanism and sparse coding principle of the human visual system (HVS). In the feature extraction step, we first divide an image into different reg...
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We describe a novel mobile visual search system based on the saliency mechanism and sparse coding principle of the human visual system (HVS). In the feature extraction step, we first divide an image into different regions using the saliency extraction algorithm. Then scale-invariant feature transform (SIFT) descriptors in all regions are extracted while regional identities are preserved based on their various saliency levels. According to the sparse coding principle in the HVS, we adopt a local neighbor preserving Hash function to establish the binary sparse expression of the SIFT features. In the searching step,
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