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This paper proposes a novel neural network learning the essential mapping function between the low resolution and high resolution image for Image superresolution problem. In our approach, patch recurrence property of small patches in natural image are utilized as a prior to train the network. An autoencoder neutral network is designed to reconstruct the high resolution patches. The constraint that...
Despite the existence of many state-of-the-art face verification systems, the use of complex features and/or high order recognition models in these systems limits their application in devices with low computation power or low latency requirement. In this paper, we approach the problem by performing verification using simple linear distance model. We introduce a novel probability-based distance metric...
Segmentation of video objects from background is a popular computer vision topic and has many important applications. Most existing methods are either computationally expensive or requiring manual initialization, static cameras, and/or rigid scenes. In a previous work, we proposed a joint spatio-temporal linear regression algorithm to automatically cluster the sparse edge/corner pixels in each video...
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