1 resultado para Developmental Psychology
em Massachusetts Institute of Technology
Filtro por publicador
- Repository Napier (2)
- Aberdeen University (1)
- Aberystwyth University Repository - Reino Unido (11)
- Adam Mickiewicz University Repository (1)
- AMS Tesi di Dottorato - Alm@DL - Università di Bologna (1)
- Aquatic Commons (10)
- Archivo Digital para la Docencia y la Investigación - Repositorio Institucional de la Universidad del País Vasco (2)
- Aston University Research Archive (14)
- Biblioteca Digital da Produção Intelectual da Universidade de São Paulo (3)
- Biblioteca Digital da Produção Intelectual da Universidade de São Paulo (BDPI/USP) (1)
- Biblioteca Digital de Teses e Dissertações Eletrônicas da UERJ (5)
- BORIS: Bern Open Repository and Information System - Berna - Suiça (41)
- Boston University Digital Common (1)
- Brock University, Canada (8)
- Bucknell University Digital Commons - Pensilvania - USA (6)
- CaltechTHESIS (4)
- Cambridge University Engineering Department Publications Database (5)
- CentAUR: Central Archive University of Reading - UK (13)
- Chinese Academy of Sciences Institutional Repositories Grid Portal (21)
- Coffee Science - Universidade Federal de Lavras (1)
- CORA - Cork Open Research Archive - University College Cork - Ireland (3)
- CUNY Academic Works (1)
- Deakin Research Online - Australia (34)
- DI-fusion - The institutional repository of Université Libre de Bruxelles (1)
- Digital Commons @ DU | University of Denver Research (4)
- Digital Commons at Florida International University (56)
- DigitalCommons - The University of Maine Research (2)
- DigitalCommons@The Texas Medical Center (2)
- DigitalCommons@University of Nebraska - Lincoln (3)
- DRUM (Digital Repository at the University of Maryland) (12)
- Duke University (17)
- eResearch Archive - Queensland Department of Agriculture; Fisheries and Forestry (5)
- Fachlicher Dokumentenserver Paedagogik/Erziehungswissenschaften (3)
- Greenwich Academic Literature Archive - UK (9)
- Helda - Digital Repository of University of Helsinki (28)
- Indian Institute of Science - Bangalore - Índia (7)
- Instituto Superior de Psicologia Aplicada - Lisboa (4)
- Massachusetts Institute of Technology (1)
- Memoria Académica - FaHCE, UNLP - Argentina (6)
- Ministerio de Cultura, Spain (6)
- Nottingham eTheses (1)
- Plymouth Marine Science Electronic Archive (PlyMSEA) (10)
- Portal de Revistas Científicas Complutenses - Espanha (7)
- QUB Research Portal - Research Directory and Institutional Repository for Queen's University Belfast (164)
- Queensland University of Technology - ePrints Archive (270)
- Repositório digital da Fundação Getúlio Vargas - FGV (2)
- Repositório Institucional da Universidade de Brasília (1)
- Repositório Institucional UNESP - Universidade Estadual Paulista "Julio de Mesquita Filho" (6)
- Repositorio Institucional Universidad EAFIT - Medelin - Colombia (1)
- Research Open Access Repository of the University of East London. (1)
- SAPIENTIA - Universidade do Algarve - Portugal (1)
- SerWisS - Server für Wissenschaftliche Schriften der Fachhochschule Hannover (1)
- Universidad de Alicante (2)
- Universidad del Rosario, Colombia (6)
- Universidade Complutense de Madrid (1)
- Universidade Metodista de São Paulo (2)
- Universitat de Girona, Spain (4)
- Universitätsbibliothek Kassel, Universität Kassel, Germany (2)
- Université de Lausanne, Switzerland (1)
- Université de Montréal, Canada (4)
- University of Connecticut - USA (3)
- University of Michigan (3)
- University of Queensland eSpace - Australia (102)
- University of Washington (3)
- WestminsterResearch - UK (2)
- Worcester Research and Publications - Worcester Research and Publications - UK (1)
Resumo:
This memo describes the initial results of a project to create a self-supervised algorithm for learning object segmentation from video data. Developmental psychology and computational experience have demonstrated that the motion segmentation of objects is a simpler, more primitive process than the detection of object boundaries by static image cues. Therefore, motion information provides a plausible supervision signal for learning the static boundary detection task and for evaluating performance on a test set. A video camera and previously developed background subtraction algorithms can automatically produce a large database of motion-segmented images for minimal cost. The purpose of this work is to use the information in such a database to learn how to detect the object boundaries in novel images using static information, such as color, texture, and shape. This work was funded in part by the Office of Naval Research contract #N00014-00-1-0298, in part by the Singapore-MIT Alliance agreement of 11/6/98, and in part by a National Science Foundation Graduate Student Fellowship.