We propose a well-founded method of ranking a pool of m trained classifiers by their suitability for the current input of n instances. It can be used when dynamically selecting a s...
We present a method to automatically learn object categories from unlabeled images. Each image is represented by an unordered set of local features, and all sets are embedded into...
This paper develops an efficient new method for 3D partial shape retrieval. First, a Monte Carlo sampling strategy is employed to extract local shape signatures from each 3D model...
In this work, we propose a new paradigm called power emulation, which exploits hardware acceleration to drastically speedup power estimation. Power emulation is based on the obser...
Sorting is a memory intensive operation whose performance is greatly affected by the amount of memory available as work space. When the input size is unknown or available memory s...