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Generalized belief propagation GBP is a region-based belief propagation algorithm which can get good convergence in Markov random fields. However, the computation time is too heavy to use in practical engineering applications. This paper proposes a method to accelerate the efficiency of GBP. A caching technique and chessboard passing strategy are used to(More)
This paper presents a novel object-oriented stereo matching on multi-scale superpixels to generate a low-resolution depth map. It overcomes the classic downsampling methods' disadvantages, such as boundary blurring, outlier enlargement and foreground objects merging to background, etc. The approach we exploited is to segment the image in three scales'(More)
AMract Users of future generation wireless information services will have diverse needs for voice, data, and potentially even viclco communications in a wide variety of circumstances. For users in dense, inner-city areas, low power PCS technology should be ideal. Vehicular-based users traveling at high speeds will need high power cellular technology. For(More)
The paper is to propose a framework to qualitatively and quantitatively evaluate five of state-of-the-art over-segment approaches. Moreover upon over-segments evaluation, an efficient approach is developed for dense stereo matching through robust higher-order MRFs and graph cut based optimization, which combines the conventional data and smoothness terms(More)
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