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A new image segmentation method is proposed to combine the edge information with the feature-space method, K-Means clustering. A procedure called seam processing, which is computationally efficient, is employed to search for horizontal and vertical seams that contain edge information. By transforming the spatial coordinates based on the seam detection results, the edge information can be added to...
A new photo retrieval system for mobile devices is proposed in this paper. The system can be used to search for photos with similar spatial layout effectively and efficiently, and it adopts a new algorithm that extracts features of image regions based on hardware K-Means clustering. Since K-Means is computationally intensive for real-time applications in embedded systems, it is necessary to accelerate...
A fast and efficient approach for color image segmentation is proposed. In this work, a new quantization technique for HSV color space is implemented to generate a color histogram and a gray histogram for K-Means clustering, which operates across different dimensions in HSV color space. Compared with the traditional K-Means clustering, the initialization of centroids and the number of cluster are...
A robust video object segmentation algorithm for complex conditions in surveillance systems is proposed in this paper. This algorithm contains an unsupervised K-Means background clustering technique to model the temporal distribution in RGB domain for each spatial position. Based on the proposed background model, the object mask generation process integrates noise reduction, cast shadow cancellation,...
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