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

Harshshikha Nandan | Zendy; Manisha Jindal | Zendy; Arsh Arsh | Zendy; Debanjan Konar | Zendy

Open Access

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

Author(s) -

Harshshikha Nandan,

Manisha Jindal,

Arsh Arsh,

Debanjan Konar

Publication year - 2015

Publication title -

international journal of computer applications

Language(s) - English

Resource type - Journals

ISSN - 0975-8887

DOI - 10.5120/20687-3554

Subject(s) - computer science , artificial neural network , object (grammar) , artificial intelligence , binary number , development (topology) , extraction (chemistry) , data mining , pattern recognition (psychology) , chromatography , arithmetic , mathematics , mathematical analysis , chemistry

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 SelfOrganizing Neural Networks (SONN) architectures as BiDirectional 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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