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Harris Operator Corner Detection using Sliding Window Method

by Jyoti Malik, Ratna Dahiya, G. Sainarayanan
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 22 - Number 1
Year of Publication: 2011
Authors: Jyoti Malik, Ratna Dahiya, G. Sainarayanan
10.5120/2546-3489

Jyoti Malik, Ratna Dahiya, G. Sainarayanan . Harris Operator Corner Detection using Sliding Window Method. International Journal of Computer Applications. 22, 1 ( May 2011), 28-37. DOI=10.5120/2546-3489

@article{ 10.5120/2546-3489,
author = { Jyoti Malik, Ratna Dahiya, G. Sainarayanan },
title = { Harris Operator Corner Detection using Sliding Window Method },
journal = { International Journal of Computer Applications },
issue_date = { May 2011 },
volume = { 22 },
number = { 1 },
month = { May },
year = { 2011 },
issn = { 0975-8887 },
pages = { 28-37 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume22/number1/2546-3489/ },
doi = { 10.5120/2546-3489 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:08:18.382014+05:30
%A Jyoti Malik
%A Ratna Dahiya
%A G. Sainarayanan
%T Harris Operator Corner Detection using Sliding Window Method
%J International Journal of Computer Applications
%@ 0975-8887
%V 22
%N 1
%P 28-37
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper, Harris Corner Detector is proposed as a corner detection technique to extract palmprint features in the form of corners. Here, hamming distance similarity measurement using sliding window method is used as a feature matching method for the corners detected. The aim of using hamming distance method for corner matching is the non-dependency of the method with the number of corners detected. So, the comparison (matching) time will be constant with hamming distance feature matching method. We used the same feature matching technique in edge detection and got good results. In this paper, palmprint features are analyzed on different sigma, threshold and radius values. Experiments were developed on a database of 600 images from 100 individuals, with five image samples per individual for training and one image sample per individual for testing. The experimental results indicate that using Harris corner detector and Hamming distance using sliding window, recognition rate of 97.5% can be achieved.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Palmprint Feature extraction Harris Corner Detector Hamming distance

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