Edgar Lobaton and Ram Vasudevan and Ron Alterovitz and Ruzena Bajcsy

EECS Department, University of California, Berkeley

Technical Report No. UCB/EECS-2011-89

August 5, 2011

http://www2.eecs.berkeley.edu/Pubs/TechRpts/2011/EECS-2011-89.pdf

Local photometric descriptors are a crucial low level component of numerous computer vision algorithms. In practice, these descriptors are constructed to be invariant to a class of transformations. However, the development of a descriptor that is simultaneously robust to noise and invariant under general deformation has proven difficult. In this paper, we introduce the Topological-Attributed Relational Graph (T-ARG), a new local photometric descriptor constructed from homology that is provably invariant to locally bounded deformation. This new robust topological descriptor is backed by a formal mathematical framework. We apply T-ARG to a set of benchmark images to evaluate its performance. Results indicate that T-ARG significantly outperforms traditional descriptors for noisy, deforming images.

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BibTeX citation:

@techreport{Lobaton:EECS-2011-89,
    Author= {Lobaton, Edgar and Vasudevan, Ram and Alterovitz, Ron and Bajcsy, Ruzena},
    Title= {Robust Topological Features for Deformation Invariant Image Matching},
    Year= {2011},
    Month= {Aug},
    Url= {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2011/EECS-2011-89.html},
    Number= {UCB/EECS-2011-89},
    Abstract= {Local photometric descriptors are a crucial low level component of numerous computer vision algorithms. In practice, these descriptors are constructed to be invariant to a class of transformations. However, the development of a descriptor that is simultaneously robust to noise and invariant under general deformation has proven difficult. In this paper, we introduce the Topological-Attributed Relational Graph (T-ARG), a new local photometric descriptor constructed from homology that is provably invariant to locally bounded deformation. This new robust topological descriptor is backed by a formal mathematical framework. We apply T-ARG to a set of benchmark images to evaluate its performance. Results indicate that T-ARG significantly outperforms traditional descriptors for noisy, deforming images.},
}

EndNote citation:

%0 Report
%A Lobaton, Edgar 
%A Vasudevan, Ram 
%A Alterovitz, Ron 
%A Bajcsy, Ruzena 
%T Robust Topological Features for Deformation Invariant Image Matching
%I EECS Department, University of California, Berkeley
%D 2011
%8 August 5
%@ UCB/EECS-2011-89
%U http://www2.eecs.berkeley.edu/Pubs/TechRpts/2011/EECS-2011-89.html
%F Lobaton:EECS-2011-89