Arnav Bhavsar

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We propose an example-based approach for enhancing resolution of range-images. Unlike most existing methods on range-image superresolution (SR), we do not employ a colour image counterpart for the range-image. Moreover, we use only a small set of range-images to construct a dictionary of exemplars. Considering the importance of edges in range-image SR, our(More)
We propose an example-based super-resolution (SR) framework, which uses a single input image and, unlike most of the SR methods does not need an external high resolution (HR) dataset. Our SR approach is based in sparse representation framework, which depends on a dictionary, learned from the given test image across different scales. In addition, our sparse(More)
Image denoising is a classical and fundamental problem in image processing community. An important challenge in image denoising is to preserve image details while removing noise. However, most of the approaches depend on smoothness assumption of natural images to produce results with smeared edges, hence, degrading the quality. To address this concern, we(More)
Depth map sensed by low-cost active sensor is often limited in resolution, whereas depth information achieved from structure from motion or sparse depth scanning techniques may result in a sparse point cloud. Achieving a high-resolution (HR) depth map from a low resolution (LR) depth map or densely reconstructing a sparse non-uniformly sampled depth map are(More)
In this work, we consider the problem of recognition of object manipulation actions. This is a challenging task for real everyday actions, as the same object can be grasped and moved in different ways depending on its functions and geometric constraints of the task. We propose to leverage grasp and motion-constraints information, using a suitable(More)
We consider the problem of detecting and localizing a human action from continuous action video from depth cameras. We believe that this problem is more challenging than the problem of traditional action recognition as we do not have the information about the starting and ending frames of an action class. Another challenge which makes the problem difficult,(More)