DEVELOPMENT OF A GENERAL METHOD FOR DETECTING A SPECIFIED FAMILY OF CURVES IN A DIGITAL IMAGE

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The problem of detecting all digital line components contained in a black-white image is one of the most fundamental problems in pattern recognition. In this study we have developed efficient algorithms for detecting all of digital line components. We also developed space-efficient algorithms for detecting all of digital curve components. This is the first linear-space algorithm for the purpose for detecting curve components and in addition the first to define a family of digital curves using least possible number of parameters. More concretely, key ideas are duality transform between points and lines and efficient search in an arrangment of lines. One advantage of the proposed algorithms to be distinguished from the existing ones is that they can detect all possible line and curve components satisfying the conditions without missing one. We also analyzed the computational complexity of the problem itself and algorithms to establish the optimality of the proposed algorithms. Experimental results show the practical usefullness and effectiveness of the proposed methods.