Genetic Algorithm for Line Extraction

The extraction of lines from an image can beseen as a hard optimization problem that includes many local optima, each line of the image beinga diierent optimum. Genetic algorithms GA are powerful stochastic optimization techniques and are considered to solve this problem. The optimization model for line extraction is shown to be equivalent t o H o u g h Transform HT. It has the advantage of evaluating the objective function at a minimum numberof points in the parameter space, while HT must build the whole parameter space. The algorithm has a breakdown point of approximately 95, which is the maximum percentage of outliers tolerated by the algorithm. GA also allows the extraction of few lines simultaniously, in a single pass. Mutiple passes allow the extraction of a much larger number of lines by reducing the set of points at each pass. The use of GA and robust statistics in the extraction model makes it possible to generalize the extraction for any t ype of geometric primitive.