View-Hosting: Streaming views on a single screen


View-Hosting: Streaming views on a single screen – Scene localization is a key component of many applications, including computer vision and image retrieval, as the goal is to identify a scene from a set of available view-aware sensors. In this work, we propose an iterative algorithm for scene localization under various camera viewpoint parameters. The proposed method is based on a low-bandwidth feature representation framework and it computes the optimal number of parameters by solving an optimization problem over the feature vectors. For this purpose, we adopt a new convolutional neural network to compute an optimal number of parameters while minimizing the cost associated with using the feature representation. Finally, we propose a deep learning model to handle the challenging scene localization problem. Experimental results on image retrieval, scene localization and object tracking show that the proposed method can be a highly promising step for scene localization.

This paper proposes a new algorithm for training deep generative models of visual attention. First, a Convolutional Neural Network is trained to recognize visual attention patterns. Then a deep learning algorithm is applied to extract features from the visual attention patterns. The proposed algorithm is evaluated on both synthetic and real datasets. Using the real dataset, the proposed algorithm is able to learn features from the visual attention patterns, and to predict the task of visual attention using a combination of multiple deep learning algorithms. Furthermore, a deep learning algorithm is applied to the image retrieval problem of the future. Our results demonstrate that the proposed algorithm achieves good accuracy, and comparable to the state of the art when learned with Convolutional Neural Networks (CNNs) as part of the training data.

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View-Hosting: Streaming views on a single screen

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  • Convex Tensor Decomposition with the Deterministic Kriging Distance

    Image Processing with Generative Adversarial NetworksThis paper proposes a new algorithm for training deep generative models of visual attention. First, a Convolutional Neural Network is trained to recognize visual attention patterns. Then a deep learning algorithm is applied to extract features from the visual attention patterns. The proposed algorithm is evaluated on both synthetic and real datasets. Using the real dataset, the proposed algorithm is able to learn features from the visual attention patterns, and to predict the task of visual attention using a combination of multiple deep learning algorithms. Furthermore, a deep learning algorithm is applied to the image retrieval problem of the future. Our results demonstrate that the proposed algorithm achieves good accuracy, and comparable to the state of the art when learned with Convolutional Neural Networks (CNNs) as part of the training data.


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