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Author Ruichek, Y. ♦ Postaire, J.-G.
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©1995
Language English
Subject Domain (in DDC) Technology ♦ Engineering & allied operations
Subject Keyword Stereo vision ♦ Cameras ♦ Constraint optimization ♦ Hopfield neural networks ♦ Vehicle safety ♦ Image reconstruction ♦ Pixel ♦ Feature extraction ♦ Data mining ♦ Image edge detection
Abstract The neural method for achieving real-time obstacle detection in front of a car using linear stereo vision is presented. The key problem is the linear stereo correspondence problem which consists in identifying features in two images that are projections of the same physical entity in the 3D world. The edge points extracted from each image are first classified into two classes. The problem is then decomposed into two identical sub-problems, each of them consisting in matching features of the same class. Each sub-problem is formulated as an optimisation task where an energy function, which represents the constraints on the solution, is to be minimised. Finally, a Hopfield neural network is used to solve the optimisation task. The preliminary classification of the edges allows one to implement the matching process as two networks running in parallel. Experimental results, using real stereo images, are presented to demonstrate the effectiveness of the proposed method.
Description Author affiliation: Centre d'Autom. de Lille, Univ. des Sci. et Tech. de Lille, Villeneuve d'Ascq, France (Ruichek, Y.; Postaire, J.-G.)
ISBN 0780325591
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 1995-10-22
Publisher Place Canada
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Size (in Bytes) 577.13 kB
Page Count 6
Starting Page 3902
Ending Page 3907


Source: IEEE Xplore Digital Library