Improving a genetic algorithm segmentation by means of a fast edge detection technique

This paper presents a new hybrid range image segmentation approach. Two separate techniques are applied consecutively. First, an edge based segmentation technique extracts the edge points—creases and jumps—contained in the given range image. Then, by using only the edge point position information, the boundaries are computed. Secondly, the points clustered into each region are approximated by single surfaces through a Genetic Algorithm (GA). The GA takes advantage of previous edge representation finding the surface parameters that best fit each region. It works in a local way, according to the boundary information, reducing considerably the required CPU time. Experimental results with different range images are presented; moreover a comparison using either the edge detection stage or not is given.