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Texture identification is an important preliminary step in many computer vision applications. There exists supervised and unsupervised approaches to solve this problem. One of the widely used unsupervised technique is KMeans which identifies the region of image based on clustering. The disadvantage of the KMeans technique is that it is an off-line approach that needs all the data prior to processing...
This paper addresses the problem of markerless tracking of a human in full 3D with a high-dimensional (29D) body model. Most work in this area has been focused on achieving accurate tracking in order to replace marker-based motion capture, but do so at the cost of relying on relatively clean observing conditions. This paper takes a different perspective, proposing a body-tracking model that is explicitly...
The ability to learn and recognize human activities of daily living (ADLs) is important in building pervasive and smart environments. In this paper, we tackle this problem using the hidden semi-Markov model. We discuss the state-of-the-art duration modeling choices and then address a large class of exponential family distributions to model state durations. Inference and learning are efficiently addressed...
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