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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/50982

Title: On Disparity Matching in Stereo Vision via a Neural Network Framework
Contributors: 國立臺灣海洋大學電機工程學系
Keywords: stereo vision
disparity matching
self-creating network
back-propagation network
neural networks
self-organizing feature map
Date: 1999-03
Issue Date: 2018-11-06T02:10:23Z
Publisher: Proceedings of the National Science Council -Part A
Abstract: Abstract: This paper presents a neural framework for dealing with the problem of disparity matching in stereo
vision. Two different types of neural networks are used in this framework: one is called the vitality
conservation (VC) network for learning clustering, and the other is the back-propagation (BP) network
for learning disparity matching. The VC network utilizes a vitality conservation principle to facilitate
self-development in network growing. The training process of VC is smooth and incremental; it not only
achieves the biologically plausible learning property, but also facilitates systematic derivations for training
parameters. Using the [intensity, variation, orientation, x, y] of each pixel (or a block) as the training
vector, the VC network dismembers the input image into several clusters, and results can be used by the
BP network to achieve accurate matching. Unlike the conventional k-means and self-organizing feature
map (SOFM), VC is a self-creating network; the number of clusters is self-organizing and need not be
pre-specified. The BP network, using differential features as input training data, can learn the functional
relationship between differential features and the matching degree. After training, the BP network is first
used to generate an initial disparity (range) map. With the clustering results and the initial map, a matching
algorithm that incorporates the BP network is then applied to recursively refine the map in a cluster-bycluster
manner. In the matching process, useful constraints, such as a epipolar line, ordering, geometry
and continuity, are employed to reduce the occurrence of mismatching. The matching process continues
until all clusters are matched. Empirical results indicate that the proposed framework is very promising
for applications in stereo vision.
Relation: 23(5) pp.665-678
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/50982
Appears in Collections:[電機工程學系] 期刊論文

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