Abstract
A genetic algorithm for the detection and extraction of linear features in a gray scale image is presented. Conventional techniques for detection of linear features based on template matching and the Hough Transform, rely on an exhaustive search of the solution space, thus rendering them computationally intensive, whereas techniques based on heuristic search in a state-space graph are prone to being trapped in a suboptimal solution state. On account of its building blocks property the genetic algorithm alleviates the need for exhaustive search and the stochastic nature of the genetic algorithm operators makes it robust to the presence of local optima in the solution space. Experimental results on gray scale images bring out the advantages of the genetic algorithm in comparison to the template matching-based and Hough Transform-based techniques for linear feature extraction.
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