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Truncated convex models (TCM) are a special case of pair-wise random fields that have been widely used in computer vision. However, by restricting the order of the potentials to be at most two, they fail to capture useful image statistics. We propose a natural generalization of TCM to high-order random fields, which we call truncated max-of-convex models (TMCM). The energy function of TMCM consists...
Mobile cloud computing (MCC) permits mobile users to access the ample cloud computing resources. Mobile applications are pervasive and provide supplementary functionality on mobile devices, but these type of applications consume more battery of the mobile devices. Offloading the power hungry part of the application into the mobile cloud is one of the approach to prolong the battery lifetime of mobile...
We consider the task of learning the parameters of a segmentation model that assigns a specific semantic class to each pixel of a given image. The main problem we face is the lack of fully supervised data. We address this issue by developing a principled framework for learning the parameters of a specific-class segmentation model using diverse data. More precisely, we propose a latent structural support...
Supervised learning of a parts-based model can be formulated as an optimization problem with a large (exponential in the number of parts) set of constraints. We show how this seemingly difficult problem can be solved by (i) reducing it to an equivalent convex problem with a small, polynomial number of constraints (taking advantage of the fact that the model is tree-structured and the potentials have...
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