A Three-Dimensional Self-Organizing Neural Network Architecture For Three-Dimensional Object Extraction From A Noisy Perspective

被引：0

作者：

Dasgupta, Kousik ^{[1
]}

Bhattacharyya, Siddhartha ^{[2
]}

Dutta, Paramartha ^{[3
]}

机构：

[1] Kalyani Govt Engn Coll, Dept Comp Sci & Engn, Kalyani 741235, W Bengal, India

[2] Univ Burdwan, Inst Technol, Dept Comp Sci Informat Technol, Burdwan 713104,, W Bengal, India

[3] Visva Bharati Univ, Dept Comp & Syst Sci, Santini Ketan 731235, W Bengal, India

来源：

FIRST INTERNATIONAL CONFERENCE ON ADVANCED COMPUTING 2009 (ICAC 2009) | 2009年

关键词：

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

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.

引用

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页码：141 / +

页数：3

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