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Techniques d'optimisation en traitement d'image et vision par ordinateur et en transport logistique

Abstract : The works presented in this thesis are related to new techniques of optimization for the solution of four important problems of two fields : transport logistics, computer vision and image processing. They are nonconvex optimization problems of very large dimensions for which the research of good solution methods is always of actuality. Our work is based mainly on the DC programming and DCA that have been successfully applied in various fields of applied sciences, including machine learning. It is motivated and justified by the robustness and the good performance of DC programming and DCA in comparison with the existing methods. The thesis is divided into five chapters. The first chapter, which serves as a reference for other chapters, presents some tools from the bases of DC programming and DCA. The second chapter discusses the problem of supply chain design at the strategic level, which can be formulated as a mixed integer linear program. We treat this problem by DC algorithms via exact penalty on the basis of suitable DC decompositions and a technique for finding a good starting point. The third chapter is concerned with the discrete tomography applied to the problem of binary image reconstruction. This problem is solved by three different approaches based on DCA. In the fourth chapter, we study the problem of image segmentation using Fuzzy C-Means clustering via DCA. A nonlinear estimate of the fundamental matrix in computer vision by the trust-region method based on the truncated conjugate gradient method or DCA is developed in the final chapter
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Trong Phuc Nguyen. Techniques d'optimisation en traitement d'image et vision par ordinateur et en transport logistique. Ordinateur et société [cs.CY]. Université Paul Verlaine - Metz, 2007. Français. ⟨NNT : 2007METZ019S⟩. ⟨tel-01748976⟩

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