A new algorithm is proposed to detect a corner of a thinned binary image that is represented by an eight-connected contour chain code. The algorithm is based on chain-coded image, deriving the slope between each code,...
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A new algorithm is proposed to detect a corner of a thinned binary image that is represented by an eight-connected contour chain code. The algorithm is based on chain-coded image, deriving the slope between each code, analyze the series of chain code, and finally decide the existence of corner at the Current pixel location. The work assumes that the pre-processing processes on the image, namely thinning and digitization, have been done. Two weighted parameters identified as significant factors in determining the accuracy of the corner detection algorithm are discussed. The parameters are the length of segment and threshold value. Computational phases to derive values of rows and columns given a series of chain code are also given in detail. The algorithm can be used to interpret line drawing that represents three-dimensional object.
This paper is focusing on extracting chain codes of a thinned binary image using two soft computing approaches, i.e. Differential Evolution (DE) and Particle Swarm Optimization (PSO). The problem is to find a continuo...
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ISBN:
(纸本)9781424453306
This paper is focusing on extracting chain codes of a thinned binary image using two soft computing approaches, i.e. Differential Evolution (DE) and Particle Swarm Optimization (PSO). The problem is to find a continuous route which covers all of the nodes of the image. The motivation is that finding such a route is complicated when it has many branches. Literature review shows that these approaches have not been used for solving such problem. In addition, the result shows that the proposed PSO has a better performance than the proposed DE for solving the problem.
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