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Identification of Precise Object among Various Objects using Sparse Coding

by Giby Jose, P. Manimegalai
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 154 - Number 10
Year of Publication: 2016
Authors: Giby Jose, P. Manimegalai
10.5120/ijca2016912239

Giby Jose, P. Manimegalai . Identification of Precise Object among Various Objects using Sparse Coding. International Journal of Computer Applications. 154, 10 ( Nov 2016), 24-28. DOI=10.5120/ijca2016912239

@article{ 10.5120/ijca2016912239,
author = { Giby Jose, P. Manimegalai },
title = { Identification of Precise Object among Various Objects using Sparse Coding },
journal = { International Journal of Computer Applications },
issue_date = { Nov 2016 },
volume = { 154 },
number = { 10 },
month = { Nov },
year = { 2016 },
issn = { 0975-8887 },
pages = { 24-28 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume154/number10/26528-2016912239/ },
doi = { 10.5120/ijca2016912239 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:59:54.549664+05:30
%A Giby Jose
%A P. Manimegalai
%T Identification of Precise Object among Various Objects using Sparse Coding
%J International Journal of Computer Applications
%@ 0975-8887
%V 154
%N 10
%P 24-28
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In order to identify the exact coconut object form the image, the following methodology is proposed. The input image is preprocessed. Its quality is enhanced using histogram equalization to produce a better result for region-based feature extraction. The edges are then detected in the image segmentation process, as this information is most essential for the classifier algorithm. After detecting the edges the CHT algorithm is applied to identify the coconut. The performance measures viz. Recognition rate, Precision, Recall and simulation time are computed. To enhance the performance, the proposed algorithm is applied and the performance parameters are tabulated. A comparative study is made between the CHT and proposed algorithm to validate the superiority of the proposed algorithm. Sparse Representation-Based Classification (SRC) is face recognition innovation in current years, which has effectively addressed the recognition problem. It recognizes an object based on the training images made available in the gallery. In this paper, SRC has been intended for coconut object identification.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Coconut Identification Sparse Representation-Based Classification (SRC) Recognition Rate.

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