Titlebook: Optimization for Computer Vision; An Introduction to C Marco Alexander Treiber Book 2013 Springer-Verlag London 2013

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Introduction,criterion. Consequently, the field of optimization is worth studying for everyone being seriously interested in computer vision. In this chapter, some expressions being of widespread use in literature dealing with optimization are clarified first. Furthermore, a classification framework is presented

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Linear Programming and the Simplex Method,nd very efficiently. Solutions of linear objective functions can only be unique if additional constraints exist. While the constraints bound the solution space, the linear nature of the objective ensures that the solution must be located at the border of this bounded solution space. In the case of l

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Correspondence Problems,rresponding counterpart in a model set, at some point of their proceeding. Examples are object recognition or image registration schemes. Because an exhaustive evaluation of all possible combinations of individual assignments is infeasible in terms of runtime, more sophisticated approaches are requi

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Graph Cuts,consistency for neighboring pixels) are quite common in computer vision. This kind of energy can be represented well by Markov Random Fields (MRFs). If we have to take a binary decision, e.g., in binary segmentation, where each pixel has to be labeled as “object” or “background,” the MRF can be supp

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Marco Alexander Treibeream of optical images having FullHD resolution with 30 frames per second (fps) and a depth map of the scene. At present, our system is able to recognize continuous fingerspelling gestures and sequences of digits in Russian and Kazakh sign languages (SL). Our gesture vocabulary contains 52 fingerspel