Abstract
This chapter presents a new algorithm for edge detection based on the hybridization of quantum computing and metaheuristics . The main idea is the use of cellular automata (CA) as a complex system for image modeling, and quantum algorithms as a search strategy. CA is a grid of cells which cooperate in parallel and have local interaction with their neighbors using simple transition rules. The aim is to produce a global function and exhibit new structures. CA is used to find a subset of a large set of transition rules, which leads to the final result, in our case: edge detection. To tackle this difficult problem, the authors propose the use of a Quantum Genetic Algorithm (QGA) for training CA to carry out edge detection tasks. The efficiency and the enforceability of QGA are demonstrated by visual and quantitative results. A comparison is made with the Conventional Genetic Algorithm . The obtained results are encouraging.
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Djemame, S., Batouche, M. (2018). A Hybrid Metaheuristic Algorithm Based on Quantum Genetic Computing for Image Segmentation. In: Bhattacharyya, S. (eds) Hybrid Metaheuristics for Image Analysis. Springer, Cham. https://doi.org/10.1007/978-3-319-77625-5_2
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DOI: https://doi.org/10.1007/978-3-319-77625-5_2
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