Dense disparity estimation for spherical images based on belief propagation
Tutor / directorFrossard, Pascal
Tipus de documentProjecte Final de Màster Oficial
Data2009-06
Condicions d'accésAccés obert
Llevat que s'hi indiqui el contrari, els
continguts d'aquesta obra estan subjectes a la llicència de Creative Commons
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Reconeixement-NoComercial-SenseObraDerivada 3.0 Espanya
Abstract
A lot of applications in computer vision are based on a pixel-labelling problem, such as stereo
matching, image restoration or object segmentation. In the last years great advances have been
achieved in dense disparity estimation, being Graph Cuts and Belief Propagation two of the
most outstanding algorithms. Particularly, Belief Propagation has some characteristics which
make it very interesting to deal with, i.e. powerful message passing and high flexibility.
Furthermore, working with omnidirectional cameras, instead of standard cameras, a smaller
number of images would be needed because of their wider field of view and it would allow
reconstructing the 3D scene in an easier way.
This project aims to adapt the Belief Propagation algorithm to spherical stereo images. In
addition, as working with spherical images, we should take into account that these images will
be projected on a sphere, being then the pixels at different distances between them. Thus, the
project also aims to improve the algorithm adding a weighting function which considers the
distance between the points on the sphere.
The project contains the general description of the proposed framework as well as an analysis and evaluation of the results obtained after its implementation.
Fitxers | Descripció | Mida | Format | Visualitza |
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Dense disparity ... on belief propagation.pdf | 1,506Mb | Visualitza/Obre |