S. Karthikeyan Mehmet Emre Sargin, Swapna Joshi, B.S. Manjunath, Scott Grafton "Generalized subspace based high dimensional density estimation" IEEE International Conference on Image Processing(ICIP), Brussels, Belgium, 2011.
 V Jagadeesh, N Vu, B.S. Manjunath, "Multiple Structure Tracing in 3D Electron Micrographs". MICCAI 2011, Toronto, Canada, Sep. 2011.
 S. Karthikeyan, Utkarsh Gaur, B.S. Manjunath, Scott Grafton "Probabilistic subspace-based learning of shape dynamics modes for multi-view action recognition" IEEE International Conference on Computer Vision (ICCV) workshop, PERHAPS 2011, Barcelona, Spain, 2011.
 Currently not available.
 Automatic interpretation of Transmission Electron Micrograph (TEM) volumes is central to advancing current understanding of neural circuitry. In the context of TEM image analysis, tracing 3D neuronal structures is a signicant problem. This work proposes a new model using the conditional random eld (CRF) framework with higher order potentials for tracing multiple neuronal structures in 3D. The model consists of two key features. First, the higher order CRF cost is designed to enforce label smoothness in 3D and capture rich textures inherent in the data. Second, a technique based on semi-supervised edge learning is used to propagate high condence structural edges during the tracing process. In contrast to predominantly edge based methods in the TEM tracing literature, this work simultaneously combines regional texture and learnt edge features into a single framework. Experimental results show that the proposed method outperforms more traditional models in tracing neuronal structures from TEM stacks.
 We propose a human action recognition algorithm by capturing a compact signature of shape dynamics from multi-view videos. First, we compute R transforms and its temporal velocity on action silhouettes from multiple views to generate a robust low level representation of shape. The spatio-temporal shape dynamics across all the views is then captured by fusion of eigen and multiset partial least squares modes. This provides us a lightweight signature which is classified using a probabilistic subspace similarity technique by learning inter-action and intra-action models. Quantitative and qualitative results of our algorithm are reported on MuHAVi a publicly available multi-camera multiaction dataset.
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