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A Comparative Study on the Development of Binary Object Extraction System using Different Self Organizing Neural Network

by Harshshikha Nandan, Manisha Jindal, Arsh, Debanjan Konar
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 117 - Number 22
Year of Publication: 2015
Authors: Harshshikha Nandan, Manisha Jindal, Arsh, Debanjan Konar
10.5120/20687-3554

Harshshikha Nandan, Manisha Jindal, Arsh, Debanjan Konar . A Comparative Study on the Development of Binary Object Extraction System using Different Self Organizing Neural Network. International Journal of Computer Applications. 117, 22 ( May 2015), 24-29. DOI=10.5120/20687-3554

@article{ 10.5120/20687-3554,
author = { Harshshikha Nandan, Manisha Jindal, Arsh, Debanjan Konar },
title = { A Comparative Study on the Development of Binary Object Extraction System using Different Self Organizing Neural Network },
journal = { International Journal of Computer Applications },
issue_date = { May 2015 },
volume = { 117 },
number = { 22 },
month = { May },
year = { 2015 },
issn = { 0975-8887 },
pages = { 24-29 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume117/number22/20687-3554/ },
doi = { 10.5120/20687-3554 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:00:06.924929+05:30
%A Harshshikha Nandan
%A Manisha Jindal
%A Arsh
%A Debanjan Konar
%T A Comparative Study on the Development of Binary Object Extraction System using Different Self Organizing Neural Network
%J International Journal of Computer Applications
%@ 0975-8887
%V 117
%N 22
%P 24-29
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Accurately Extraction of a binary object from a noisy perspective has been a daunting task in the field of pattern recognition. Several techniques have been tried to optimally solve the problem of denoising of object over the decades. In this paper, different binary object extraction methods are reviewed which are basically guided by different Self-Organizing Neural Networks (SONN) architectures as Bi-Directional Self Organizing Neural Network (BDSONN), multi-Layer Self Organizing neural Network (MLSONN) and quantum version of MLSONN (QMLSONN). The result shows that QMLSONN outperforms over other network architectures in terms of time and also it restores shape of the object with great accuracy.

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Index Terms

Computer Science
Information Sciences

Keywords

Binary object extraction Fuzzy context sensitive thresholding Quantum computing Multilayer self-organizing neural network Quantum back-propagation algorithm Bidirectional self-organizing neural network System transfer index Quantum multilayer self-organizing neural network.

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