A New Algorithm for Polygonal Approximation Based on Ant Colony Optimization

Abstract

Abstract. In shape analysis a crucial step consists in extracting meaningful features from digital curves. Dominant points are those points with curvature extreme on the curve that can suitably describe the curve both for visual perception and for recognition. In this paper we present a novel method that combines the dominant point detection and the ant colony optimization search. The excellent results have been compared both to works using an optimal search approach and to works based on exact approximation strategy.

Generalized Hough Transform for Shape Matching

“…As we can see from the numerical results, all the simulations based on using the GHT in shape matching present the best accuracy values, but they take a longer time. Finally, we want to stress that the best results are obtained by using the dominant point detection method proposed in [4]. The presented recognition system represents a right compromise in term of accuracy and time, confirming the effectiveness and the efficiency of the method proposed for shape…”

“…Luckily, there exist some heuristic approaches (genetic algorithms and tabu search) which can find solutions very close to the global optimal one in a relative short time. In [4] we have presented a method that combines the dominant point detection and the ant colony optimization search. The method is inspired by the ant colony search suggested in [18] but it results in a much more efficient and effective approximation algorithm.…”

“…In [4] we have presented a novel method for dominant point detection. Since of the efficiency and the efficacy of such approximation algorithm, we have used this dominant point selection to get the set of points for the matching phase.…”

On the Detection of Visual Features from Digital Curves Using a Metaheuristic Approach

“…We can summarize the differences and the contribution of our approach against the one in [1] in four main aspects: less parameter dependance, automatic choosing of starting points and number of distributed ants, updating of the best tour during search (Step 4 of the Poly_by_ACS Algorithm) and refining. First preliminary results of our approach have been presented in [29]. In the new version we have proposed in this paper we have introduced the possibility to delete very close dominant points, by obtaining a significant reducing of N P with a negligible in- creasing of approximation error.…”