Ankur Handa

ahanda(at)doc(dot)ic(dot)ac(dot)uk
handa(dot)ankur(at)gmail(dot)com

Hello World! I am a PhD student in the Department of Computing under the supervision of Dr. Andrew Davison.

Publications

Scalable Active Matching. Ankur Handa, Margarita Chli, Hauke Strasdat, Andrew J. Davison [pdf] [poster] [old video] [new video]
CVPR 2010.
[Abstract]
Matching image features given general priors on their locations and correlations among them, can be efficiently solved guided by "do computation only which is really required" paradigm. Active Matching works exactly in this manner exploiting these priors to decide the best choice available at hand at each step that would yield the maximum information gain. However, this sequentially guided approach expends a considerable effort in deciding the importance of choices thereby limiting the scalability of the approach to only a conservative number of features. In this paper, borrowing the theory and underlying principles from Active Matching, we relax the general structure of the problem it maintains to conservative approximations with only a slight accuracy penance while increasing the efficiency by high proportions. One approach, CLAM (Chow-Liu Active Matching), reduces the dense graph maintained by Active Matching to a maximum-spanning tree while the other, SubAM (Subset Active Matching) works by building a tree of subsets(clusters) of features. In essence, both approaches thwart the unmanageably explosive computation of a canonical dense bayesian graph structure maintained by Active Matching algorithm.

Technical Reports

Applications of Legendre-Fenchel Transformation. Ankur Handa, Richard A. Newcombe, Adrien Angeli, Andrew J. Davison [pdf][bibtex]
Sept 2011
This report is a modest attempt to explain some useful details and minutiae associated with Legendre-Fenchel transformation. It is not a full-bore investigation into the topic but it is useful if you are getting started with variational methods and wanting to implement standard energy based minimisation methods e.g. Image denoising, Optical Flow, Superresolution. Most of the details can be easily adapted to other new energy formulations based on variatonal methods involving primarily the notorious L1 norm. Together with Richard, Adrien and Andy, I spent some time this summer trying to understand variational based theory made popular by GPU4Vision. All these various methods explained in the report are extremely amenable to GPUs as well as very trivial to implement. Most of the implementations would be made available very shortly. We would also like to thank Thomas Pock, Hauke Strasdat as well as Luis Pizarro for helpful discussions.

Past
Slightly ancient and odd looking home-page. [Old Home Page]

Links