Abstract
In computational 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. Many approaches have been developed for detecting dominant points. In this paper we present a novel method that combines the dominant point detection and the ant colony optimization search. The method is inspired by the ant colony search (ACS) suggested by Yin in [1] but it results in a much more efficient and effective approximation algorithm. The excellent results have been compared both to works using an optimal search approach and to works based on exact approximation strategy.
Highlights
Computer imaging has developed as an interdisciplinary research field whose focus is on the acquisition and processing of visual information by computer and has been widely used in object recognition, image matching, target tracking, industrial dimensional inspection, monitoring tasks, etc
For each image we have evaluated the initial number of points, N, the number of detected dominant points, Np, and the approximation error between the original curve and the corresponding optimal polygon, E2
In this work we have presented a novel method for approximating a digital curve
Summary
Computer imaging has developed as an interdisciplinary research field whose focus is on the acquisition and processing of visual information by computer and has been widely used in object recognition, image matching, target tracking, industrial dimensional inspection, monitoring tasks, etc. Wu and Wang [4] proposed the curvature-based polygonal approximation for dominant point detection. Approaches based on genetic algorithms [15,16] and tabu search [17] have been proposed to solve the polygonal approximation problem and they obtain better results than most of the local optimal methods. Horng [18] proposed a dynamic programming approach to improve the fitting quality of polygonal approximation by combining the dominant point detection and the dynamic programming. The exploration strategy searches for new regions, and once it finds a good region the exploitation heuristic further intensifies the search for this area In this context, metaheuristics encompass several wellknown approaches such as genetic algorithm (GA), simulated annealing, tabu search (TS), scatter search, ant colony optimization and particle swarm optimization. In this paper we present a method that combines the dominant point detection and the ant colony optimization search.
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