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A Review Paper on Object Detection for Improve the Classification Accuracy and Robustness using different Techniques

by Divya Patel, Pankaj Kumar Gautam
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 112 - Number 11
Year of Publication: 2015
Authors: Divya Patel, Pankaj Kumar Gautam
10.5120/19708-1465

Divya Patel, Pankaj Kumar Gautam . A Review Paper on Object Detection for Improve the Classification Accuracy and Robustness using different Techniques. International Journal of Computer Applications. 112, 11 ( February 2015), 5-7. DOI=10.5120/19708-1465

@article{ 10.5120/19708-1465,
author = { Divya Patel, Pankaj Kumar Gautam },
title = { A Review Paper on Object Detection for Improve the Classification Accuracy and Robustness using different Techniques },
journal = { International Journal of Computer Applications },
issue_date = { February 2015 },
volume = { 112 },
number = { 11 },
month = { February },
year = { 2015 },
issn = { 0975-8887 },
pages = { 5-7 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume112/number11/19708-1465/ },
doi = { 10.5120/19708-1465 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:49:10.737669+05:30
%A Divya Patel
%A Pankaj Kumar Gautam
%T A Review Paper on Object Detection for Improve the Classification Accuracy and Robustness using different Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 112
%N 11
%P 5-7
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Object detection is a computer technology that connected to image processing and computer vision that deal with detecting instance objects of certain class in digital images and videos. Object detection is a challenging problem in vision based computer applications. It is used to identifying that whether in scene or image object is been there or not. In this review paper, we are going to present different techniques and methods for detecting or recognizing object with various benefits like efficiency, accuracy, robustness etc.

References
  1. Ashwin Deshpande, " Object Recognition Using Large Datasets".
  2. Chunhui Gu, Pablo Arbelaez, Yuanqing Lin, Kai Yu and Jitendra Malik, "Multi-Component Models for Object Detection", European Conference on Computer Vision, pp. 445-458, 2012.
  3. Nima Razavi, Juergen Gall and Luc Van Gool, "Scalable Multi-class Object Detection", IEEE Conference on Computer Vision and Pattern Recognition, pp. 1505-1512, 2011.
  4. Nima Razavi, Juergen Gall, Pushmeet Kohli, Luc Van Gool, "Latent Hough Transform for Object Detection", European Conference on Computer Vision, pp. 312-325, 2012.
  5. Bjorn Ommer and Jitendra Malik, "Multi-Scale Object Detection by Clustering Lines", International Conference on Computer Vision, pp. 484-491, 2009.
  6. Sinisa Segvic, Zoran Kalafatic and Ivan Kovacek, "Sliding window object detection without spatial clustering of raw detection responses", IFAC Symposium on Robot Control, pp. 114-119, 2012.
  7. A. Geiger, P. Lenz, and R. Urtasun. Are we ready for autonomous driving? the KITTI vision benchmark suite. In CVPR, 2012.
  8. Bojan Pepik, Michael Stark, Peter Gehler and Bernt Schiele, "Occlusion Patterns for Object Class Detection", IEEE Conference on Computer Vision and Pattern Recognition, 2013.
  9. Xiaofeng Ren and Deva Ramanan, "Histograms of Sparse Codes for Object Detection", IEEE Conference on Computer Vision and Pattern Recognition, pp. 3246-3253, 2013.
  10. Sushmita Mitra , Witold Pedrycz , Bishal Barman, "Shadowed c-means: Integrating fuzzy and rough clustering", Elsevier
Index Terms

Computer Science
Information Sciences

Keywords

Object detection robustness.

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