To cite this record: http://ddd.uab.cat/record/52590
Depth Recovery of Complex Surfaces from Texture-less Pairs of Stereo Images
Kumar, S.
Kumary, M.
Sukavanamy, N.
Balasubramaniany, R.
Bhargava, R.

Date: 2009
Abstract: In this paper, a novel framework is presented to recover the 3D shape information of a complex surface using its texture-less stereo images. First a linear and generalized Lambertian model is proposed to obtain the depth information by shape from shading (SfS) using an image from stereo pair. Then this depth data is corrected by integrating scale invariant features (SIFT) indexes. These SIFT indexes are defined by means of disparity between the matching invariant features in rectified stereo images. The integration process is based on correcting the 3D visible surfaces obtained from SfS using these SIFT indexes. The SIFT indexes based improvement of depth values which are obtained from generalized Lambertian reflectance model is performed by a feed-forward neural network. The experiments are performed to demonstrate the usability and accuracy of the proposed framework.
Rights: Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades. Creative Commons
Language: Anglès.
Document: article ; recerca ; publishedVersion
Subject: Aproximació de funcions ; Xarxes Neuronals ; Model de reflectància ; Característiques constants d'escala ; Forma d'ombrejat ; Aproximación de funciones ; Redes Neuronales ; Modelo de reflectancia ; Características constantes de escala ; Forma de sombreado ; Function Approximation ; Neural Network ; Reflectance Model ; Scale Invariant Features ; Shape from Shading
Published in: ELCVIA : Electronic Letters on Computer Vision and Image Analysis, V. 8 n. 1 (2009) p. 44-56, ISSN 1577-5097



13 p, 2.1 MB

The record appears in these collections:
Articles > Published articles > ELCVIA : Electronic Letters on Computer Vision and Image Analysis

 Record created 2010-01-18, last modified 2014-06-08



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