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Number 8

Counting Objects using Convolution based Pattern Matching Technique

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  • Jaydeo K. Dharpure and M. B. Potdar and Manoj Pandya 2013. Counting Objects using Convolution based Pattern Matching Technique. International Journal of Applied Information Systems. 5, 8 (June 2013), 14-19. DOI=http://dx.doi.org/10.5120/ijais450964 
  • @article{10.5120/ijais2017451568,
author = {Jaydeo K. Dharpure and M. B. Potdar and Manoj Pandya},
title = {Counting Objects using Convolution based Pattern Matching Technique},
journal = {International Journal of Applied Information Systems},
issue_date = {June 2013},
volume = {5},
number = {},
month = {June},
year = {2013},
issn = {},
pages = {14-19},
numpages = {},
url = {/archives/volume5/number8/469-0964},
doi = { 10.5120/ijais13-450964},
publisher = { xA9 2012 by IJAIS Journal},
address = {}
}

  • %1 450964
%A Jaydeo K.  Dharpure
%A M.  B.  Potdar
%A Manoj Pandya
%T Counting Objects using Convolution based Pattern Matching Technique
%J International Journal of Applied Information Systems
%@ 
%V 5
%N 
%P 14-19
%D 2013
%I  xA9 2012 by IJAIS Journal

Abstract

In this paper, counting objects techniques are proposed for fast pattern matching algorithm based on normalized cross correlation and convolution technique which are widely used in image processing application. Pattern matching can be used to recognize and/or locate specific objects in an image. It is one of the emerging areas in computational object counting. In this paper, introduces a new pattern matching technique called convolution based on pattern matching algorithm. Many different pattern matching techniques have been developed but more efficient and robust methods are needed. The pattern matching algorithm is used to identify the patterns similar present in image. With the patterns, identify the similarity measures of the given pattern to count the object present in the given image. An experimental evaluation is carried out to estimate the performance of the proposed efficient pattern matching algorithm for remote sensing as well as common images in terms of estimation of execution times, efficiency and compared the results with an existing conventional methods.

References

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Keywords

Convolution, Normalized Cross Correlation, Pattern Matching, Thresholding and Template Matching

Index Terms

Computer Science
Information Sciences