A Study on Optimal Design of Hough Transform Algorithm for Real Time Vision

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We promoted our research to realize a real time mechanism to extract figure patterns compounded of line segments. We utilized the Hough transform as the basic feature extraction method. We examined and evaluated from the view points of the processing cost for straight line extraction, the relation between a compound figure pattern and its feature, real time processing, applicability to the image sequences, and we clarified the following results.In order to realize real time processing, we reduced the computation cost by applying the Hough transform not to the whole image but to the partial image and by combining the respective results. We constructed an optimal procedure for the setting of Hough transform parameters from the view point of the relation between the number of space division and the resolution of line segment and the computation cost. From the view point of the extension of the feature extraction mechanism, we improved the feature extraction algorithms for the practical use. As a meta-mechanism of line segment extraction, we introduced the center-fine/peripheral-coarse model which simulates the human vision to search the interesting point or area, and realized the global feature extraction mechanism. Furthermore, we investigated the description of digital line segment, and proposed a new representation method and an algorithm using chain code description. To confirm the effectivity and the applicability of the proposed methods and algorithms to real time vision, we implemented these algorithms to an autonomous robot system for soccer game and experimented, and evaluated under a real and practical condition.