NDLI: General layered neural network for stereo disparity detection

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International 1989 Joint Conference on Neural Networks

General layered neural network for stereo disparity detection

Content Provider	IEEE Xplore Digital Library
Author	Maeda, E.
Copyright Year	1990
Abstract	The general layered network for stereo (GLNS), a method for creating the general layered neural network to get stereo disparity maps, is presented. By using GLNS, disparity maps can be obtained without iterative calculations because it is a three-layered neural network. GLNS accepts gray levels in stereo images and output stereo disparity maps of the input images. A multistage learning method for training a large-scale network without falling into worthless local minimum points is proposed. The method consists of four learning stages that progress from simple tasks in a small network to complicated ones in a large network. The parameters from one task are used as initial parameters in the succeeding stages. Connection parameters of GLNS can be decided by the learning. Feature extraction and any heuristics or knowledge about images are not required. The matching property and sensitivity of GLNS can be easily designed. Random binary patterns with various disparities between left and right are used as training sets. GLNS can be changed by a small amount of additive learning into specific neural networks for stereo which are tuned to specific kinds of stereo images
Starting Page	487
Ending Page	490
File Size	379232
Page Count	4
File Format	PDF
DOI	10.1109/IJCNN.1990.137610
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1990-06-17
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Content Type	Text
Resource Type	Article

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