Fast 2D to 3D conversion using a clustering-based hierarchical search in a machine learning framework

Herrera Conejero, José Luis; Blanco Adán, Carlos Roberto del y García Santos, Narciso (2014). Fast 2D to 3D conversion using a clustering-based hierarchical search in a machine learning framework. En: "3DTV-Conference: The True Vision - Capture, Transmission and Display of 3D Video (3DTV-CON)", 02/07/2014 - 04/07/2014, Budapest, Hungary. pp. 1-4.

Descripción

Título: Fast 2D to 3D conversion using a clustering-based hierarchical search in a machine learning framework
Autor/es:
  • Herrera Conejero, José Luis
  • Blanco Adán, Carlos Roberto del
  • García Santos, Narciso
Tipo de Documento: Ponencia en Congreso o Jornada (Artículo)
Título del Evento: 3DTV-Conference: The True Vision - Capture, Transmission and Display of 3D Video (3DTV-CON)
Fechas del Evento: 02/07/2014 - 04/07/2014
Lugar del Evento: Budapest, Hungary
Título del Libro: 3DTV-Conference: The True Vision - Capture, Transmission and Display of 3D Video (3DTV-CON)
Título de Revista/Publicación: 2014 3DTV-CONFERENCE: THE TRUE VISION - CAPTURE, TRANSMISSION AND DISPLAY OF 3D VIDEO (3DTV-CON)
Fecha: 2014
Materias:
Palabras Clave Informales: 2D-to-3D conversion, fast conversion, 3D inference, machine learning, hierarchical search, SURF descriptors, database clustering
Escuela: E.T.S.I. Telecomunicación (UPM)
Departamento: Ingeniería de Sistemas Telemáticos [hasta 2014]
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

Automatic 2D-to-3D conversion is an important application for filling the gap between the increasing number of 3D displays and the still scant 3D content. However, existing approaches have an excessive computational cost that complicates its practical application. In this paper, a fast automatic 2D-to-3D conversion technique is proposed, which uses a machine learning framework to infer the 3D structure of a query color image from a training database with color and depth images. Assuming that photometrically similar images have analogous 3D structures, a depth map is estimated by searching the most similar color images in the database, and fusing the corresponding depth maps. Large databases are desirable to achieve better results, but the computational cost also increases. A clustering-based hierarchical search using compact SURF descriptors to characterize images is proposed to drastically reduce search times. A significant computational time improvement has been obtained regarding other state-of-the-art approaches, maintaining the quality results.

Más información

ID de Registro: 36209
Identificador DC: http://oa.upm.es/36209/
Identificador OAI: oai:oa.upm.es:36209
URL Oficial: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6874736&pageNumber%3D136968
Depositado por: Memoria Investigacion
Depositado el: 05 Jul 2015 10:56
Ultima Modificación: 05 Jul 2015 10:56
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