Multi-Resolution Video Super-resolution with Multilayer Biomedical Volumesets


Multi-Resolution Video Super-resolution with Multilayer Biomedical Volumesets – We present a new unsupervised learning model — VSRV-UVM – for the purpose of learning the pose and segmentation of 3D objects with low computational cost for video data collection. VSRV-UVM utilizes nonlinear and nonconvex optimization over $n x_i$-dimensional multi-resolution images. This model is useful to develop new algorithms for large-scale 3D object segmentation of high resolution data, or for image segmentation of images collected during training and testing tasks for different applications. We show how VSRV-UVM is able to achieve significant improvement in the pose and segmentation of data, as compared to baseline CNN-VM methods. We further show how it learns to predict the pose of object objects from their geometric expressions; however, the proposed model is not suitable for large-scale object segmentation due to its strong computational cost and low sample complexity. We implement and evaluate the proposed VSRV-UVM method in an unsupervised learning setting.

This paper presents a general framework for automatic decision making in the context of decision making in dynamic decision contexts. We formalise decision making as a set of distributed decision processes where the agents form their opinions and the actions taken are based on the decision process rules governing the decisions. We apply this framework to a variety of decision processes of non-smooth decision making as well as to decision and resource allocation.

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Multi-Resolution Video Super-resolution with Multilayer Biomedical Volumesets

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  • Attention based Recurrent Neural Network for Video Prediction

    Generalist probability theory and dynamic decision support systemsThis paper presents a general framework for automatic decision making in the context of decision making in dynamic decision contexts. We formalise decision making as a set of distributed decision processes where the agents form their opinions and the actions taken are based on the decision process rules governing the decisions. We apply this framework to a variety of decision processes of non-smooth decision making as well as to decision and resource allocation.


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