Experimental Research on Intelligent Image Segmentation System

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1 Rough Segmentation of Specified Object RegionWe segmented a color image into several regions using color similarity, and used the result as an initial scene interpretation. We used the partial 3-D information derived from the initial segmentation to interpret the scene in details. The validity of our method was shown by applying it to several kinds of real scenes. Moreover, we developed a method of interpreting more complicated scenes using the depth information obtained by our stereo matching method.2 Accurate Region Extraction from Rough SegmentationWe found that the existing methods were not applicable to objects like humans that consist of multiple parts such as faces and clothes. Then, we developed a method which detects an accurate boundary between a specified object and the background assuming it consists of several parts.3 Design and Preliminary Implementation of High-Speed Processor for Image SegmentationConventional high-speed image processors are not sufficient for unage segmentation. Construction of 2-D histogram for initial color image segnientation and spatial convolution (2Ox2O) for feature extraction are major processing to speed up the image segmentation. A high-speed processors are constructed by adding these modules to a structure-variable image processor. We checked the effectiveness of the processing time by applying it to the image segmentation.