DocumentCode
2551883
Title
3-D Scene Modelling from Multiple Images using Radial Basis Function Networks
Author
Grum, Matthew ; Bors, Adrian G.
Author_Institution
Dept. of Comput. Sci., Univ. of York, York
fYear
2007
fDate
27-29 Aug. 2007
Firstpage
105
Lastpage
110
Abstract
A new approach for modelling multiple 3-D objects from a sparse set of images taken from various viewpoints is proposed in this paper. A voxel model of the scene is estimated from the given set of images using the space carving algorithm. An implicit radial basis function (RBF) network is used afterwards to model the voxel data. The multiorder function is chosen as the kernel function due to its property of enforcing smoothing constraints in the first three derivatives. A suitable initialization is proposed for the RBF parameters. Displacements of surface patches along epipolar lines are used to update the centers of basis functions leading to the modelling errors minimization. The proposed method is used to model a complex 3-D scene.
Keywords
image reconstruction; image representation; radial basis function networks; smoothing methods; solid modelling; 3D scene modelling; RBF network; epipolar lines; image representation; kernel function; multiorder function; multiple images; radial basis function network; scene reconstruction; smoothing method; space carving algorithm; surface displacement; voxel model; Cameras; Computer science; Electronic mail; Error correction; Image reconstruction; Interpolation; Kernel; Layout; Radial basis function networks; Smoothing methods;
fLanguage
English
Publisher
ieee
Conference_Titel
Machine Learning for Signal Processing, 2007 IEEE Workshop on
Conference_Location
Thessaloniki
ISSN
1551-2541
Print_ISBN
978-1-4244-1565-6
Electronic_ISBN
1551-2541
Type
conf
DOI
10.1109/MLSP.2007.4414290
Filename
4414290
Link To Document