NDLI: A three-dimensional self-organizing neural network architecture for three-dimensional object extraction from a noisy perspective

Content Provider	IEEE Xplore Digital Library
Author	Dasgupta, K. Bhattacharyya, S. Dutta, P.
Copyright Year	2009
Description	Author affiliation: Department of Computer Science Information Technology, University Institute of Technology, The University of Burdwan, Golapbag (North), 713 104, India (Bhattacharyya, S.) \|\| Department of Computer Science and Engineering, Kalyani Government Engineering College, 741 235, West Bengal, India (Dasgupta, K.) \|\| Department of Computer and System Sciences, Visva-Bharati University, Santiniketan - 731 235, India (Dutta, P.)
Abstract	Processing of three-dimensional image data for quality enhancement, segmentation and analysis is a challenging proposition due to the enormity of the underlying data content as well due to the inadequacy of data description standards. Extraction of objects from 3-dimensional image information is no exception. In this article, a novel three-dimensional neural network architecture is presented for faithful extraction of 3-dimensional objects from a noisy perspective. The proposed network architecture operates in a self-supervised mode assisted by fuzzy measures. Results of application of the proposed architecture are demonstrated on several synthetic and real life three-dimensional binary voxelized images. The efficacy of the architecture in different types of noises indicates encouraging avenues.
Starting Page	141
Ending Page	147
File Size	1874777
Page Count	7
File Format	PDF
ISBN	9781424447862
DOI	10.1109/ICADVC.2009.5378210
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-12-13
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Graphics Computer science Solid modeling Image analysis Image processing Neural networks Data visualization Computer architecture Data engineering Data mining
Content Type	Text
Resource Type	Article

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