CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

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

by Ankur Chauhan, Sanjay Kumar
journal cover thumbnail

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.

References
  1. R. Lefort, R. Fablet and J. -M. Boucher, "Object recognition using proportion-based prior information: Application to fisheries acoustics", Pattern Recognition Letters January 2011, Volume 32, Issue 2, Pages 153-158
  2. Zhangzhang Si and Song-Chun Zhu, "Learning AND-OR Templates for Object Recognition and Detection", IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 35, NO. 9, SEPTEMBER 2013
  3. Z. Si and S. -C. Zhu, "Learning Hybrid Image Templates (HIT) by Information Projection," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 34, no. 7, pp. 1354-1367, July 2012.
  4. Juergen Gall, Angela Yao, NimaRazavi, Luc Van Gool, and Victor Lempitsky, "Hough Forests for Object Detection, Tracking, and Action Recognition", IEEE Transactionson Pattern Analysis and machine intelligence, VOL. X, NO. X, JANUARY 2011
  5. Myung Jin Choi, Joseph J. Lim, Antonio Torralba, Alan S. Willsky, "A Tree-Based Context Model for Object Recognition", Computer Science and Artificial Intelligence Laboratory Technical Report, MIT-CSAIL-TR-2010-050 October 29, 2010
  6. Gabriel L. Oliveira, Erickson R. Nascimento, Antonio W. Vieira, Mario F. M. Campos, "Sparse Spatial Coding: A Novel Approach for Efficient and Accurate Object Recognition", 2012 IEEE International Conference onRobotics and Automation (ICRA)
  7. Canny Edge Detection, March 23, 2009
  8. ZhenhuaGuo,LeiZhang,David Zhang, "A Completed Modeling of Local Binary Pattern Operator for Texture Classification", IEEE transaction on image processing, 2010
  9. MasoudNosrati, RonakKarimi, Mehdi Hariri, "Detecting Circular Shapes From Areal Images Using Median Filter and CHT", World Applied Programming, Vol (2), Issue (1), . 49-54, January 2012
  10. Bangpeng Yao, Li Fei-Fei, "Modeling Mutual Context of Object and Human Posein Human-Object Interaction Activities", 2010 IEEE Conference onComputer Vision and Pattern Recognition (CVPR)
  11. Kevin Lai,Liefeng Bo,XiaofengRen, Dieter Fox,"A Scalable Tree-Based Approachfor Joint Object and Pose Recognition", Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence
  12. ArjanGijsenij, and Theo Gevers, "Color Constancy UsingNatural Image Statistics and Scene Semantics", IEEE Transactions on Pattern Analysis And Machine Intelligence, Vol . 33, No. 4, April 2011, pp 687
  13. WenshuoGao, Lei Yang, Xiaoguang Zhang, Huizhong Liu, "An Improved Sobel Edge Detection", 978-1-4244-5540-9/10/$26. 00 ©2010 IEEE
  14. Alvaro Collet, Manuel Martinez, Siddhartha S. Srinivasa, "The MOPED framework: Object Recognition and Pose Estimation forManipulation", 2010 3rd IEEE International Conference on Computer Science and Information Technology (ICCSIT)
Index Terms

Computer Science
Information Sciences

Keywords

Object recognition review accurate modeling issue and challenges proposed model.

Powered by PhDFocusTM