Learning 3D structure from 2D images using LBP features

Herrera Conejero, José Luis and Blanco Adán, Carlos Roberto del and García Santos, Narciso (2014). Learning 3D structure from 2D images using LBP features. In: "IEEE International Conference on Image Processing (ICIP 2014)", 27/10/2014 - 30/10/2014, Paris, France. pp. 2022-2025. https://doi.org/10.1109/ICIP.2014.7025405.

Description

Title: Learning 3D structure from 2D images using LBP features
Author/s:
  • Herrera Conejero, José Luis
  • Blanco Adán, Carlos Roberto del
  • García Santos, Narciso
Item Type: Presentation at Congress or Conference (Article)
Event Title: IEEE International Conference on Image Processing (ICIP 2014)
Event Dates: 27/10/2014 - 30/10/2014
Event Location: Paris, France
Title of Book: IEEE International Conference on Image Processing (ICIP 2014)
Date: 2014
Subjects:
Freetext Keywords: 2D-to-3D Conversion, Depth maps, Machine Learning, Local Binary Patterns, Bilateral Filtering
Faculty: E.T.S.I. Telecomunicación (UPM)
Department: Señales, Sistemas y Radiocomunicaciones
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

An automatic machine learning strategy for computing the 3D structure of monocular images from a single image query using Local Binary Patterns is presented. The 3D structure is inferred through a training set composed by a repository of color and depth images, assuming that images with similar structure present similar depth maps. Local Binary Patterns are used to characterize the structure of the color images. The depth maps of those color images with a similar structure to the query image are adaptively combined and filtered to estimate the final depth map. Using public databases, promising results have been obtained outperforming other state-of-the-art algorithms and with a computational cost similar to the most efficient 2D-to-3D algorithms.

Funding Projects

TypeCodeAcronymLeaderTitle
Government of SpainTEC2010-20412UnspecifiedUnspecifiedUnspecified

More information

Item ID: 37595
DC Identifier: http://oa.upm.es/37595/
OAI Identifier: oai:oa.upm.es:37595
DOI: 10.1109/ICIP.2014.7025405
Deposited by: Memoria Investigacion
Deposited on: 14 Oct 2015 15:49
Last Modified: 14 Oct 2015 15:49
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