1 resultado para object analysis
em Acceda, el repositorio institucional de la Universidad de Las Palmas de Gran Canaria. España
Filtro por publicador
- KUPS-Datenbank - Universität zu Köln - Kölner UniversitätsPublikationsServer (1)
- Academic Archive On-line (Stockholm University; Sweden) (1)
- Acceda, el repositorio institucional de la Universidad de Las Palmas de Gran Canaria. España (1)
- AMS Tesi di Dottorato - Alm@DL - Università di Bologna (4)
- AMS Tesi di Laurea - Alm@DL - Università di Bologna (7)
- ArchiMeD - Elektronische Publikationen der Universität Mainz - Alemanha (1)
- Aston University Research Archive (11)
- Biblioteca Digital da Produção Intelectual da Universidade de São Paulo (5)
- Biblioteca Digital da Produção Intelectual da Universidade de São Paulo (BDPI/USP) (496)
- BORIS: Bern Open Repository and Information System - Berna - Suiça (13)
- Brock University, Canada (2)
- Bulgarian Digital Mathematics Library at IMI-BAS (6)
- CentAUR: Central Archive University of Reading - UK (6)
- Cochin University of Science & Technology (CUSAT), India (4)
- Coffee Science - Universidade Federal de Lavras (2)
- Comissão Econômica para a América Latina e o Caribe (CEPAL) (1)
- Consorci de Serveis Universitaris de Catalunya (CSUC), Spain (6)
- CORA - Cork Open Research Archive - University College Cork - Ireland (1)
- Corvinus Research Archive - The institutional repository for the Corvinus University of Budapest (1)
- Dalarna University College Electronic Archive (1)
- Department of Computer Science E-Repository - King's College London, Strand, London (1)
- Digital Commons - Michigan Tech (2)
- Digital Commons at Florida International University (3)
- Digital Peer Publishing (1)
- DigitalCommons@The Texas Medical Center (2)
- Doria (National Library of Finland DSpace Services) - National Library of Finland, Finland (6)
- Institutional Repository of Leibniz University Hannover (1)
- Instituto Politécnico do Porto, Portugal (3)
- Iowa Publications Online (IPO) - State Library, State of Iowa (Iowa), United States (1)
- Massachusetts Institute of Technology (5)
- Memoria Académica - FaHCE, UNLP - Argentina (3)
- National Center for Biotechnology Information - NCBI (4)
- Portal de Revistas Científicas Complutenses - Espanha (1)
- Publishing Network for Geoscientific & Environmental Data (9)
- QSpace: Queen's University - Canada (1)
- QUB Research Portal - Research Directory and Institutional Repository for Queen's University Belfast (1)
- Repositório Científico da Universidade de Évora - Portugal (1)
- Repositório da Produção Científica e Intelectual da Unicamp (70)
- Repositório Digital da Universidade Municipal de São Caetano do Sul - USCS (1)
- Repositório Institucional da Universidade Federal do Rio Grande do Norte (1)
- Repositório Institucional UNESP - Universidade Estadual Paulista "Julio de Mesquita Filho" (10)
- RUN (Repositório da Universidade Nova de Lisboa) - FCT (Faculdade de Cienecias e Technologia), Universidade Nova de Lisboa (UNL), Portugal (10)
- Scielo Saúde Pública - SP (4)
- Universidad de Alicante (2)
- Universidad Politécnica de Madrid (25)
- Universidade do Minho (2)
- Universidade Federal do Rio Grande do Norte (UFRN) (3)
- Universita di Parma (1)
- Universitat de Girona, Spain (4)
- Université de Lausanne, Switzerland (9)
- Université de Montréal (1)
- Université de Montréal, Canada (3)
- University of Michigan (5)
- University of Queensland eSpace - Australia (207)
- Worcester Research and Publications - Worcester Research and Publications - UK (1)
Resumo:
[EN]Different researches suggest that inner facial features are not the only discriminative features for tasks such as person identification or gender classification. Indeed, they have shown an influence of features which are part of the local face context, such as hair, on these tasks. However, object-centered approaches which ignore local context dominate the research in computational vision based facial analysis. In this paper, we performed an analysis to study which areas and which resolutions are diagnostic for the gender classification problem. We first demonstrate the importance of contextual features in human observers for gender classification using a psychophysical ”bubbles” technique.