Fuzzy Energy-Based Active Contours Exploiting Local Information

Abstract : This paper presents a novel fast and robust model for active contours to detect objects in an image, based on techniques of curve evolution. The proposed model can detect objects whose boundaries are not necessarily defined by gradient, based on the minimization of a fuzzy energy. This fuzzy energy is used as the model motivation power evolving the active contour, which will stop on the desired object boundary. The fuzziness of the energy provides a balanced technique with a strong ability to reject “weak”, as well as, “strong” local minima. Also, this approach differs from previous methods, since it does not solve the Euler-Lagrange equations of the underlying problem, but, instead, calculates the fuzzy energy alterations directly. So, it converges to the desired object boundary very fast. The theoretical properties and various experiments presented demonstrate that the proposed fuzzy energy-based active contour is better and more robust than classical snake methods based on the gradient or other kind of energies.
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Stelios Krinidis, Michail Krinidis. Fuzzy Energy-Based Active Contours Exploiting Local Information. 8th International Conference on Artificial Intelligence Applications and Innovations (AIAI), Sep 2012, Halkidiki, Greece. pp.175-184, ⟨10.1007/978-3-642-33409-2_19⟩. ⟨hal-01521387⟩

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