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Reseach Article

Efficient Object Recognition using Convolution Neural Networks Theorem

by Aarushi Thakral, Shaurya Shekhar, Akila Victor
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 161 - Number 2
Year of Publication: 2017
Authors: Aarushi Thakral, Shaurya Shekhar, Akila Victor
10.5120/ijca2017913123

Aarushi Thakral, Shaurya Shekhar, Akila Victor . Efficient Object Recognition using Convolution Neural Networks Theorem. International Journal of Computer Applications. 161, 2 ( Mar 2017), 36-47. DOI=10.5120/ijca2017913123

@article{ 10.5120/ijca2017913123,
author = { Aarushi Thakral, Shaurya Shekhar, Akila Victor },
title = { Efficient Object Recognition using Convolution Neural Networks Theorem },
journal = { International Journal of Computer Applications },
issue_date = { Mar 2017 },
volume = { 161 },
number = { 2 },
month = { Mar },
year = { 2017 },
issn = { 0975-8887 },
pages = { 36-47 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume161/number2/27123-2017913123/ },
doi = { 10.5120/ijca2017913123 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:06:41.818672+05:30
%A Aarushi Thakral
%A Shaurya Shekhar
%A Akila Victor
%T Efficient Object Recognition using Convolution Neural Networks Theorem
%J International Journal of Computer Applications
%@ 0975-8887
%V 161
%N 2
%P 36-47
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Object recognition is the process of identification of an object in an image. There exist various algorithms for the same. Appearance based algorithms have demonstrated good efficiency, however, their performance gets affected adversely in the presence of clutter or when background changes are affected. We hope to overcome this issue by using Convolution Neural Network (CNN) Theorem. The approach is shape based and has been proven to work well under broad range of circumstances: varied lighting conditions, affine transformations, etc. It involves tiling, which is the phenomenon of the use of multiple layers of neurons to process small portions of the image, which are then used to obtain better representations of the image. This allows CNN to be translation-tolerant. The neural elements learn to recognize objects about which they have no previous information, this ‘learning’ mechanism is affected by the fact that representations of the image are learned by the inner layers of the deep architectures of neurons. Unlike RBM and Auto-encoder, which are capable of learning only single global weight matrix layers, the CNN theorem makes use of shared weight in convolution layers, which means that the same filter (weight bank) is used for each pixel in the layer, which reduces the memory footprint and improves performance.

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Index Terms

Computer Science
Information Sciences

Keywords

Recognition Object Neural Features Dataset Training Image