Abstract
Image matching plays an important role in many applications such as multi-modality medical imaging and multi-spectral image analysis. The role of matching is to integrate multiple sources of object information into a single image. The matching problem consists of determining the unknown transform parameters required to map one image to match the other image(20). Different non – traditional methods are used for solving this kind of problem. Among these methods are the Genetic Algorithms, Neural Networks & Simulating Annealing. Swarm Intelligence (SI) algorithms take their inspiration from the collective behavior of natural, for example, ant colonies, flocks of birds, or fish shoals, a particularly successful strandant colony optimization (ACO)(1). Ant Colony Optimization is a population-based general search technique, proposed by Dorigo(1992,1996), for the solution of difficult combinatorial problems)4). The studies show that, in nature, the ant colony is able to discover the shortest paths between the nest and food sources very efficiently, such a deposit substance is called pheromone during talking and another ants can smell it, if one of ants find a short path, it feedback on the same path and the value of pheromone on this path increases and a another ants gradually chose this path.(22) Tabu search is one of the best known heuristic to choose the next neighbor to move on. At each step, one chooses the best neighbor with respect to specific function (23). The basic idea in this paper is using Ant Colony Optimization(ACO) & Tabu Search(TS) as a success strategy for matching two images. The suggestion algorithm evaluation is a good promising solution, by providing an optimal algorithm which is executed by optimal time and coast, I believe that there is no prior research conjoining the two topics in this way. The program is written in Matlab language (6.5).
Highlights
Image matching plays an important role in many applications such as multi-modality medical imaging and multi-spectral image analysis
As shown in table(2), we generate ten random integer values for rotation and translation of each node( image) in search space, the first field of table represents the values for translating the coordinates of each point in the borders of the image, the second field represents the angles for rotation
In order to control the number of nodes in each iteration, we generate ten initial ants( nodes), this ten nodes are generated by translating and rotating the borders one of two images by the values in table(2) for obtaining new ten images with new borders, we calculate evaluation function for each new node by Euclidean law and determining the heuristic and pheromone value for each one as shown in table(3)
Summary
11,400 different species and are considered one of the most successful insects due to their highly organized colonies, sometimes consisting of millions of ants. Computer scientists began researching the behaviors of ants in the early 1990’s to discover new routing algorithms. In (ACO) meta-heuristic a colony of artificial ants cooperate in finding good solutions to difficult discrete optimization problems. 1. Advantage of Ant Colony Optimization : The important aspect of real ants’ foraging behavior that is exploited by artificial ants is the coupling between the autocatalytic(positive feedback) mechanism and the implicit evaluation of solutions. Ant Colony Optimization(ACO) is an interesting and promising result, it remains clear that as well as other metaheuristics, in many cases cannot compete with specialized local search methods. The solution construction is biased by the pheromone trails which change at run-time, the heuristic information on the problem instance and the ants’ private memory (4). Advantage of TABU : 1.cycle avoidance which saves time. 2.in ducking vigor in search(diversification exploration ): guide search to more promising or new regions of search space(18)
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