Object Recognition with an Optimized Ventral Stream Model Using Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{Clemente:evoapps12,
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author = "Eddie Clemente and Gustavo Olague and Leon Dozal and
Martin Mancilla",
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title = "Object Recognition with an Optimized Ventral Stream
Model Using Genetic Programming",
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booktitle = "Applications of Evolutionary Computing,
EvoApplications 2012: EvoCOMNET, EvoCOMPLEX, EvoFIN,
EvoGAMES, EvoHOT, EvoIASP, EvoNUM, EvoPAR, EvoRISK,
EvoSTIM, EvoSTOC",
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year = "2011",
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month = "11-13 " # apr,
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editor = "Cecilia {Di Chio} and Alexandros Agapitos and
Stefano Cagnoni and Carlos Cotta and F. {Fernandez de Vega} and
Gianni A. {Di Caro} and Rolf Drechsler and
Aniko Ekart and Anna I Esparcia-Alcazar and Muddassar Farooq and
William B. Langdon and Juan J. Merelo and
Mike Preuss and Hendrik Richter and Sara Silva and
Anabela Simoes and Giovanni Squillero and Ernesto Tarantino and
Andrea G. B. Tettamanzi and Julian Togelius and
Neil Urquhart and A. Sima Uyar and Georgios N. Yannakakis",
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series = "LNCS",
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volume = "7248",
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publisher = "Springer Verlag",
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address = "Malaga, Spain",
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publisher_address = "Berlin",
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pages = "315--325",
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organisation = "EvoStar",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-3-642-29177-7",
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DOI = "doi:10.1007/978-3-642-29178-4_32",
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size = "11 pages",
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abstract = "Computational neuroscience is a discipline devoted to
the study of brain function from an information
processing standpoint. The ventral stream, also known
as the 'what' pathway, is widely accepted as the model
for processing the visual information related to object
identification. This paper proposes to evolve a
mathematical description of the ventral stream where
key features are identified in order to simplify the
whole information processing. The idea is to create an
artificial ventral stream by evolving the structure
through an evolutionary computing approach. In previous
research, the 'what' pathway is described as being
composed of two main stages: the interest region
detection and feature description. For both stages a
set of operations were identified with the aim of
simplifying the total computational cost by avoiding a
number of costly operations that are normally executed
in the template matching and bag of feature approaches.
Therefore, instead of applying a set of previously
learnt patches, product of an off-line training
process, the idea is to enforce a functional approach.
Experiments were carried out with a standard database
and the results show that instead of 1200 operations,
the new model needs about 200 operations.",
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notes = "EvoIASP Part of \cite{DiChio:2012:EvoApps}
EvoApplications2012 held in conjunction with
EuroGP2012, EvoCOP2012, EvoBio'2012 and EvoMusArt2012",
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affiliation = "Proyecto EvoVision, Departamento de Ciencias de la
Computacion, Division de Fisica Aplicada, Centro de
Investigacion Cientifica y de Estudios Superiores de
Ensenada, Carretera Ensenada-Tijuana No. 3918, Zona
Playitas, Ensenada, 22860 B.C., Mexico",
- }
Genetic Programming entries for
Eddie Helbert Clemente Torres
Gustavo Olague
Leon Dozal
Martin Mancilla
Citations