Object categorization using biological models

Detalhes bibliográficos
Autor(a) principal: Vieira, Ana Milene Mestre
Data de Publicação: 2013
Tipo de documento: Dissertação
Idioma: por
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10400.1/3639
Resumo: Dissertação de mest., Engenharia Elétrica e Eletrónica (Tecnologias da Informação e Telecomunicação), Instituto Superior de Engenharia, Univ. do Algarve, 2013
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spelling Object categorization using biological modelsDissertação de mest., Engenharia Elétrica e Eletrónica (Tecnologias da Informação e Telecomunicação), Instituto Superior de Engenharia, Univ. do Algarve, 2013Humans are naturals at categorizing objects, i.e., at dividing them into groups depending on their features and surroundings. We do it easily and in real-time. Additionally, our Human Visual System (HVS) is the only one reliable for object detection, categorization and recognition; the latter events take place in the visual cortex, being object recognition achieved around 150-200ms, and occurring also a categorization-specific activation in prefrontal cortex before or around 100ms. This provides one of the evidences which substantiate that categorization is a more bottom-up process than recognition. Visual cortical area V1 is composed - among others - by simple and complex cells which are adjusted to different spatial frequencies (scales), orientations and disparity. These cell‟s responses were used to build a model for events detection in V1; these events are classified by type - lines and edges – and polarity - positive and negative. Being the goal of this thesis to develop a cortical model for object categorization - inspired in the HVS and based on 2D object views -, the V1 multi-scale events generated by the former model were used to accomplish that goal. In the developed categorization model the final category attributed to an object is the convergence of three similarity concepts which define in different ways the resemblance degree between an object and a certain category; the resemblance degree is therefore accomplished by comparing the V1 events between templates and objects. The resemblance degree or similarity percentage was calculated (a) on the first concept as the quotient between the number of common events between object and category templates (considering type and polarity) in all scales, and the number of object‟s events in all scales; (b) on the second concept the similarity percentage was calculated as the quotient between the number of common events between object and category templates (not considering type nor polarity) in all scales, and the number of object‟s events in all scales; (c) finally, on the third concept this ratio was calculated as the quotient between the number of common events between object and category templates (considering type and polarity) in all scales, and the category‟s “events number” in all scales. The final category assigned to an object is then (1st) a category on which the three concepts agree on and (2nd) the best scored one. For the proof of concept a database composed by 8 different categories and 10 objects per category was used; left and right profile views were chosen to represent each object. Regarding the 80 results obtained by categorizing 40 objects in both views, an average categorization success rate of 93.75% was accomplished, being 92.50% the success rate achieved for left profile, and 95.00% the one achieved for right profile; even each of the miscategorized images was attributed a category which is similar to its true one. In order to conclude the proof of concept, the model was also tested in terms of small invariance to rotation, scale and noise, having been then achieved high categorization success rates (above 82%).Rodrigues, J. M. F.SapientiaVieira, Ana Milene Mestre2014-03-14T15:44:51Z2013-07-302013-07-30T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10400.1/3639porinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-07-24T10:14:45Zoai:sapientia.ualg.pt:10400.1/3639Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T19:57:11.824633Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Object categorization using biological models
title Object categorization using biological models
spellingShingle Object categorization using biological models
Vieira, Ana Milene Mestre
title_short Object categorization using biological models
title_full Object categorization using biological models
title_fullStr Object categorization using biological models
title_full_unstemmed Object categorization using biological models
title_sort Object categorization using biological models
author Vieira, Ana Milene Mestre
author_facet Vieira, Ana Milene Mestre
author_role author
dc.contributor.none.fl_str_mv Rodrigues, J. M. F.
Sapientia
dc.contributor.author.fl_str_mv Vieira, Ana Milene Mestre
description Dissertação de mest., Engenharia Elétrica e Eletrónica (Tecnologias da Informação e Telecomunicação), Instituto Superior de Engenharia, Univ. do Algarve, 2013
publishDate 2013
dc.date.none.fl_str_mv 2013-07-30
2013-07-30T00:00:00Z
2014-03-14T15:44:51Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10400.1/3639
url http://hdl.handle.net/10400.1/3639
dc.language.iso.fl_str_mv por
language por
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
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instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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