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We developed a real-time parallel vision system (PAVIS) along with suitable parallel vision algorithms. PAVIS is designed for cost-effectiveness and flexibility, which is composed of several isotypic commercially off-the-shelf (COTS) PCs with an Ethernet switch that constitute a logically fully connected parallel system. Image partitioning method using the equal area criterion has been used for load distribution in parallel vision processing. Since the load is not the pixels but the feature points for high-level vision operations, we propose an image partitioning method named perpendicular image partitioning (PIP), which partitions image data not by the area of image but by the number of features. PIP functions as an efficient load distribution method, since image partitioning and load balancing are simultaneously performed. Real-time vision algorithm for corner detection and feature matching are also devised. Since the objects on the temporally successive sequence of image have motion continuity, we can speedup vision operations by reducing search area for corners or matching pairs along the motion trajectories found from the prey ions image frames. PAVIS is applied to the real-time depth determination problem for mobile robot navigation. The experimental results confirm the validity of proposed algorithms, and its real-time performance within the frame rate.
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Tom
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43--56
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Bibliogr. 17 poz.,Rys., wykr.,
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bwmeta1.element.baztech-article-BAT2-0001-0837