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A Pose based Object Recognition Model for Improving Learning Time and Accuracy

by Ankur Chauhan, Sanjay Kumar
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 113 - Number 3
Year of Publication: 2015
Authors: Ankur Chauhan, Sanjay Kumar
10.5120/19809-1602

Ankur Chauhan, Sanjay Kumar . A Pose based Object Recognition Model for Improving Learning Time and Accuracy. International Journal of Computer Applications. 113, 3 ( March 2015), 38-43. DOI=10.5120/19809-1602

@article{ 10.5120/19809-1602,
author = { Ankur Chauhan, Sanjay Kumar },
title = { A Pose based Object Recognition Model for Improving Learning Time and Accuracy },
journal = { International Journal of Computer Applications },
issue_date = { March 2015 },
volume = { 113 },
number = { 3 },
month = { March },
year = { 2015 },
issn = { 0975-8887 },
pages = { 38-43 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume113/number3/19809-1602/ },
doi = { 10.5120/19809-1602 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:50:02.539171+05:30
%A Ankur Chauhan
%A Sanjay Kumar
%T A Pose based Object Recognition Model for Improving Learning Time and Accuracy
%J International Journal of Computer Applications
%@ 0975-8887
%V 113
%N 3
%P 38-43
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Now in these days the computational domain contributes in a different intelligence applications such as decision making, data analysis, and face recognition and pattern detection. These applications are supporting in various real world applications. In this paper, the pattern analysis and pattern discovery task is discussed for object recognition application. Object recognition is a computational process where using the visual features are utilized for approximating the actual real world objects. In literature there are a number of object recognition models are available, those are promises to provide accurate object detection. But most of them are only produces 40-50% accurate results. In this paper basically different object recognition models are discussed which are providing guidelines for obtaining accurate model. In addition of that this paper addresses the real world issues which are required to involve for future object recognition model.

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Index Terms

Computer Science
Information Sciences

Keywords

Object recognition review accurate modeling issue and challenges proposed model.

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