Nowadays, object recognition is widely studied under the paradigm of matching local features. This work describes a genetic programming methodology that synthesizes mathematical e...
In this paper, we address the tasks of detecting, segmenting, parsing, and matching deformable objects. We use a novel probabilistic object model that we call a hierarchical defor...
Abstract. In this paper we propose a novel approach to define task-driven regularization constraints in deformable image registration using learned deformation priors. Our method ...
Ben Glocker, Nikos Komodakis, Nassir Navab, Georgi...
We introduce a new class of compiler heuristics: hybrid optimizations. Hybrid optimizations choose dynamically at compile time which optimization algorithm to apply from a set of d...
John Cavazos, J. Eliot B. Moss, Michael F. P. O'Bo...
Subgraph patterns are widely used in graph classification, but their effectiveness is often hampered by large number of patterns or lack of discrimination power among individual p...