We revisit recently proposed algorithms for probabilistic clustering with pair-wise constraints between data points. We evaluate and compare existing techniques in terms of robust...
We propose a novel algorithm for segmenting multiple motions of different types from point correspondences in multiple affine or perspective views. Since point trajectories associ...
Model-based clustering of motion trajectories can be posed as the problem of learning an underlying mixture density function whose components correspond to motion classes with dif...
: We propose a set of statistical metrics for making a comprehensive, fair, and insightful evaluation of features, clustering algorithms, and distance measures in representative sa...
Our previous work introduced a 3D particle visualization framework that viewed each data point as being a particle affected by gravitational forces. We showed the use of this tool ...