Titlebook: Energy Minimization Methods in Computer Vision and Pattern Recognition; 11th International C Marcello Pelillo,Edwin Hancock Conference proc

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https://doi.org/10.1007/978-3-658-07792-1 paper, we present Ising models for the tasks of binary clustering of numerical and relational data and discuss how to set up corresponding quantum registers and Hamiltonian operators. In simulation experiments, we numerically solve the respective Schrödinger equations and observe our approaches to yield convincing results.

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Alice Blumenthal-Dramé,Bernd Kortmannch, which extends the . algorithm to the biclustering case. In particular, we propose a new way of representing the problem, encoded as a graph, which allows to exploit dominant set to analyse both rows and columns simultaneously. The proposed approach has been tested by using a well known synthetic microarray benchmark, with encouraging results.

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Sprachwissenschaft und Volkskundepherical representation in a point of a Stiefel manifold. We show that when the temporal interval of analysis is set according to quantum efficiency principles the proposed approach outperforms the alternatives in graph discrimination.

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Das Gespräch über Literatur im Unterricht for by the general model. The main contribution of this work is the establishment of a unified theoretical framework for the restoration of turbulence-degraded images. It leads to novel turbulence recovery algorithms as well as to better understanding of known ones.

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Unified Functional Framework for Restoration of Image Sequences Degraded by Atmospheric Turbulence for by the general model. The main contribution of this work is the establishment of a unified theoretical framework for the restoration of turbulence-degraded images. It leads to novel turbulence recovery algorithms as well as to better understanding of known ones.

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https://doi.org/10.1007/978-3-319-90719-2 our adaptive depth computation achieves higher accuracy for a given computational cost than traditional fixed-structure neural networks. The presented framework extends to other tasks that use convolutional neural networks and enables trading speed for accuracy at runtime.

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https://doi.org/10.1007/1-4020-3842-9aturally..We use infimal convolution regularization as well as an automatic parameter balancing scheme to automatically determine the reliability of the motion information and reweight the regularization locally. We demonstrate that our approach yields state-of-the-art results and even is competitive with machine learning approaches.

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https://doi.org/10.1007/978-3-642-80249-2vise an optical flow method dedicated to fluid flows in which the regularization parameter has a clear physical interpretation and can be easily estimated. Experimental evaluations are presented on both synthetic and real images. Results indicate very good performance of the proposed parameter-free formulation for turbulent flow motion estimation.

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