Scale invariance for evolved interested operators

Published in Conferences Papers
  1. Leonardo Trujillo and Gustavo Olague. Scale Invariance for Evolved Interest Operators. In EvoWorkshops. 2007, 423-430. BibTeX

    @inproceedings{DBLP:conf/evoW/TrujilloO07,
    	author = "Leonardo Trujillo and Gustavo Olague",
    	title = "Scale Invariance for Evolved Interest Operators",
    	booktitle = "EvoWorkshops",
    	year = 2007,
    	pages = "423-430",
    	ee = "http://dx.doi.org/10.1007/978-3-540-71805-5_47",
    	crossref = "DBLP:conf/evoW/2007",
    	bibsource = "DBLP, http://dblp.uni-trier.de"
    }
    
  2. Mario Giacobini, Anthony Brabazon, Stefano Cagnoni, Gianni Di Caro, Rolf Drechsler, Muddassar Farooq, Andreas Fink, Evelyne Lutton, Penousal Machado, Stefan Minner, Michael O'Neill, Juan Romero, Franz Rothlauf, Giovanni Squillero, Hideyuki Takagi, Sima Uyar and Shengxiang Yang (eds.). Applications of Evolutinary Computing, EvoWorkshops 2007: EvoCoMnet, EvoFIN, EvoIASP,EvoINTERACTION, EvoMUSART, EvoSTOC and EvoTransLog, Valencia, Spain, April11-13, 2007, Proceedings 4448. Springer, 2007. BibTeX

    @proceedings{DBLP:conf/evoW/2007,
    	editor = "Mario Giacobini and Anthony Brabazon and Stefano Cagnoni and Gianni Di Caro and Rolf Drechsler and Muddassar Farooq and Andreas Fink and Evelyne Lutton and Penousal Machado and Stefan Minner and Michael O'Neill and Juan Romero and Franz Rothlauf and Giovanni Squillero and Hideyuki Takagi and Sima Uyar and Shengxiang Yang",
    	title = "Applications of Evolutinary Computing, EvoWorkshops 2007: EvoCoMnet, EvoFIN, EvoIASP,EvoINTERACTION, EvoMUSART, EvoSTOC and EvoTransLog, Valencia, Spain, April11-13, 2007, Proceedings",
    	booktitle = "EvoWorkshops",
    	publisher = "Springer",
    	series = "Lecture Notes in Computer Science",
    	volume = 4448,
    	year = 2007,
    	isbn = "978-3-540-71804-8",
    	bibsource = "DBLP, http://dblp.uni-trier.de"
    }
    
Abstract

This work presents scale invariant region detectors that apply evolved operators to extract an interest measure. We evaluate operators using their repeatability rate, and have experimentally identified a plateau of local optima within a space of possible interest operators Ω. The space Ω contains operators constructed with Gaussian derivatives and standard arithmetic operations. From this set of local extrema, we have chosen two operators, obtained by searching within Ω using Genetic Programming, that are optimized for high repeatability and global separability when imaging conditions are modified by a known transformation. Then, by embedding the operators into the linear scale space generated with a Gaussian kernel we can characterize scale invariant features by detecting extrema within the scale space response of each operator. Our scale invariant region detectors exhibit a high performance when compared with state-of-the-art techniques on standard tests.

Published in
Applications of Evolutionary Computing
Lecture Notes in Computer Science
Volume 4448
http://link.springer.com/chapter/10.1007%2F978-3-540-71805-5_47
Date of conference
11 - 13 Abril 2007
Pages
423-430
ISSN
0302-9743
ISBN
978-3-540-71805-5
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