Comparing Images Using the Hausdorff Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Matching: Similarity Measures and Algorithms
SMI '01 Proceedings of the International Conference on Shape Modeling & Applications
On the performance of artificial bee colony (ABC) algorithm
Applied Soft Computing
Journal of Global Optimization
An artificial bee colony algorithm for the leaf-constrained minimum spanning tree problem
Applied Soft Computing
Edge Potential Functions (EPF) and Genetic Algorithms (GA) for Edge-Based Matching of Visual Objects
IEEE Transactions on Multimedia
Comparison of different mutation strategies applied to artificial bee colony algorithm
ECC'11 Proceedings of the 5th European conference on European computing conference
An improved artificial bee colony algorithm based on gaussian mutation and chaos disturbance
ICSI'12 Proceedings of the Third international conference on Advances in Swarm Intelligence - Volume Part I
An evolutionary image matching approach
Applied Soft Computing
Artificial bee colony algorithm: a survey
International Journal of Advanced Intelligence Paradigms
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This paper describes a novel shape-matching approach to visual target recognition for aircraft at low altitude. An artificial bee colony (ABC) algorithm with edge potential function (EPF) is proposed to accomplish the target recognition task for aircraft. EPF is adopted to provide a type of attractive pattern for a matching contour, which can be exploited by ABC algorithm conveniently. In this way, the best match can be obtained when the sketch image translates, reorients and scales itself to maximize the potential value. In addition, the convergence proof and computational complexity for the ABC algorithm are also given in detail. Series of experimental results demonstrate the feasibility and effectiveness of our proposed approach over the traditional genetic algorithm (GA). The proposed method can also be applied to solve the target recognition problems in mobile robots, industry production lines, and transportations.