Manifold learning methods are promising data analysis tools. However, if we locate a new test sample on the manifold, we have to find its embedding by making use of the learned e...
We consider the problem of computing all-pair correlations in a warehouse containing a large number (e.g., tens of thousands) of time-series (or, signals). The problem arises in a...
This paper presents a signed distance transform algorithm using graphics hardware, which computes the scalar valued function of the Euclidean distance to a given manifold of co-di...
This paper describes Automatic Pool Allocation, a transformation framework that segregates distinct instances of heap-based data structures into seperate memory pools and allows h...
Dense stereo algorithms rely on matching over a range of disparities. To speed up the search and reduce match ambiguity, processing can be embedded in the hierarchical, or coarse-...