Dominant points detection on digital curves: A comparison between optimal and exact approaches

In this work we address the problem of closed digital curves polygonal approximation by locating a set of relevant points having high curvature, the so-called dominant points. This set of feature points plays a dominant role in shape perception by humans and contains almost complete information of a given contour. There are several methods to extract dominant points based on different approaches; we look over two heuristic techniques, based on Ant Colony Optimization (ACO) and based on Genetic Algorithm (GAs), and an original method based on Dominant Points Iterative Localization (DP1L). We compare the three algorithms by evaluating the approximation error and testing their affine transformations invariance.