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We present a novel shape from focus method for high-speed shape reconstruction in optical microscopy. While the traditional shape from focus approach heavily depends on the presence of surface texture, and requires a considerable amount of measurement time, our method is able to perform 3D reconstruction from only two images. Our method relies on the rapid projection of a binary pattern sequence,...
Consumer depth cameras, such as the Microsoft Kinect, are capable of providing frames of dense depth values at real time. One fundamental question in utilizing depth cameras is how to best extract features from depth frames. Motivated by local descriptors on images, in particular kernel descriptors, we develop a set of kernel features on depth images that model size, 3D shape, and depth edges in a...
In this paper, we present a robust framework for action recognition in video, that is able to perform competitively against the state-of-the-art methods, yet does not rely on sophisticated background subtraction preprocess to remove background features. In particular, we extend the Implicit Shape Modeling (ISM) of [10] for object recognition to 3D to integrate local spatiotemporal features, which...
In this paper, a novel relevance feedback algorithm based on SVM is proposed for 3d model retrieval. It aims to enhance retrieval accuracy in 3D model database systems. During the retrieval process, the system learns from the related samples marked by the user after each feedback, and update the training sample set with the previous returns. Thus an SVM classifier model is established and improved...
In this study, we present a representation based on a new 3D search technique for volumetric human poses which is then used to recognize actions in three dimensional video sequences. We generate a set of cylinder like 3D kernels in various sizes and orientations. These kernels are searched over 3D volumes to find high response regions. The distribution of these responses are then used to represent...
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