PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Ignazio Gallo and Elisabetta Binaghi
VISAPP 2006: International Conference on Computer Vision Theory and Applications 2005.


This work aims at defining a new method for matching correspondences in stereoscopic image analysis. The salient aspects of the method are -an explicit representation of occlusions driving the overall matching process and the use of neural adaptive technique in disparity computation. In particular, based on the taxonomy proposed by Scharstein and Szelinsky, the dense stereo matching process has been divided into three tasks: matching cost computation, aggregation of local evidence and computation of disparity values. Within the second phase a new strategy has been introduced in an attempt to improve reliability in computing disparity. An experiment was conducted to evaluate the solutions proposed The experiment is based on an analysis of test images including data with a ground truth disparity map

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
ID Code:1861
Deposited By:Ignazio Gallo
Deposited On:29 November 2005