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In this paper, we establish a baseline for object symmetry detection in complex backgrounds by presenting a new benchmark and an end-to-end deep learning approach, opening up a promising direction for symmetry detection in the wild. The new benchmark, named Sym-PASCAL, spans challenges including object diversity, multi-objects, part-invisibility, and various complex backgrounds that are far beyond...
In this paper, we proposed a pedestrian detection system based on laser and image data fusion. The high speed of laser data based location and precise of image based classification are fully explored. First, laser scanner point data is clustered into segments, each of which implies a pedestrian candidate. Then, the segments are projected to the image domain to form regions of interest (ROI) on the...
A progressive region-merging algorithm for stereo matching is put forward in this paper. Firstly, the reference image is segmented by mean shift algorithm and the initial disparity maps are calculated using loopy belief propagation method. Secondly, we derive the reliable pixels through left-right consistent constraint. In addition, we present a new approach to exclude mismatch pixels in textureless...
A new segment-based algorithm for stereo matching is put forward in this paper. Firstly, the reference image is segmented by mean shift algorithm and an adaptive window matching method using histogram of orientation gradient is presented. Secondly, a set of reliable pixels is constructed by comparing the matching cost and the mutual checking consistent between the left-right initial disparity maps,...
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