By Topic

3-D Scene Modelling from Multiple Images using Radial Basis Function Networks

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Grum, M. ; Dept. of Comput. Sci., Univ. of York, York ; Bors, A.G.

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.

Published in:

Machine Learning for Signal Processing, 2007 IEEE Workshop on

Date of Conference:

27-29 Aug. 2007