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

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3 Author(s)
Dasgupta, K. ; Dept. of Comput. Sci. & Eng., Kalyani Gov. Eng. Coll., Kalyani, India ; Bhattacharyya, S. ; Dutta, P.

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.

Published in:

Advanced Computing, 2009. ICAC 2009. First International Conference on

Date of Conference:

13-15 Dec. 2009