Google scholar arxiv informatics ads IJAIS publications are indexed with Google Scholar, NASA ADS, Informatics et. al.

Call for Paper

-

April Edition 2021

International Journal of Applied Information Systems solicits high quality original research papers for the April 2021 Edition of the journal. The last date of research paper submission is March 15, 2021.

Counting Objects using Convolution based Pattern Matching Technique

Jaydeo K. Dharpure, M. B. Potdar, Manoj Pandya Published in Pattern Recognition

International Journal of Applied Information Systems
Year of Publication: 2013
© 2012 by IJAIS Journal
10.5120/ijais13-450964
Download full text
  1. Jaydeo K Dharpure, M B Potdar and Manoj Pandya. Article: Counting Objects using Convolution based Pattern Matching Technique. International Journal of Applied Information Systems 5(8):14-19, June 2013. BibTeX

    @article{key:article,
    	author = "Jaydeo K. Dharpure and M. B. Potdar and Manoj Pandya",
    	title = "Article: Counting Objects using Convolution based Pattern Matching Technique",
    	journal = "International Journal of Applied Information Systems",
    	year = 2013,
    	volume = 5,
    	number = 8,
    	pages = "14-19",
    	month = "June",
    	note = "Published by Foundation of Computer Science, New York, USA"
    }
    

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.

Reference

  1. D. M. Tsai, C. T. Lin, (2003). Fast normalized cross correlation for defect detection. Pattern Recognition. Letter. 24 (15), 2625–2631.
  2. Di Stefano, L. , Mattoccia, S. , (2003) a. Fast template matching using bounded partial correlation. Mach. Vis. Appl. 13 (4), 213–221
  3. Federico Tombari, Stefano Mattoccia, Luigi Di Stefano, Fabio Regoli, and Riccardo Viti (2009), "A Template Analysis Methodology to Improve the Efficiency of Fast Matching Algorithms" Springer-Verlag Berlin Heidelberg, pp 100-108.
  4. Gonzalez R. C. , and Woods R. E. (2002) "Digital Image Processing" (Second Ed), Prentice Hall, ISBN-10: 0201180758.
  5. James W. Davis Mark A. Keck, (2005) 'A Two-Stage Template Approach to Person Detection in Thermal Imagery', Applications of Computer Vision, Breckenridge, Co, January 5-7.
  6. Jiun-Hung Chen, Chu-Song Chen, and Yong-Sheng Chen (2003) "Fast Algorithm for Robust Template Matching With M-Estimators" IEEE Transactions On Signal Processing, Vol. 51, No. 1, pp - 230-243.
  7. Kai Briechle and Uwe D. Hanebeck, "Template Matching using Fast Normalized Cross Correlation", Institute of the automatic control Engineering, 80290 Munchen, Germany.
  8. Lim Huey Charn, Liyana Nuraini Rasid, Shahrel A. Suandi (2010) "A Study on the Effectiveness of Different Patch Size and Shape for Eyes and Mouth Detection" International Journal on Computer Science and Engineering Vol. 02, No. 03, pp. 424-432.
  9. Luigi Di Stefano, Stefano Mattoccia and Federico Tombari (2005) "ZNCC-based template matching using bounded partial correlation" Elsevier, Pattern Recognition Letters 26 pp. 2129–2134.
  10. Mikhail J. Atallah (2001) "Faster Image Template Matching in the Sum of the Absolute Value of Differences Measure" IEEE Transactions On Image Processing, Vol. 10, No. 4, Pp. 663-659.
  11. R. Harini and C. Chandrasekar (2012) "Efficient Pattern Matching Algorithm For Classified Brain Image" International Journal of Computer Applications (0975 – 8887) Volume 57– No. 4.
  12. Rajiv Kumar Nath, 'Road Vehicle/Object Detection And Tracking Using Template', Indian Journal Of Computer Science And Engineering Vol 1 No 2, ISSN: 0976-5166, pp. 98-107.
  13. Raju Bhukya, DVLN Somayajulu (2011) "An Index Based Sequential Multiple Pattern Matching Algorithm Using Least Count", International Conference on Life Science and Technology IPCBEE vol. 3, IACSIT Press, Singapore, pp 109-113.
  14. S. Hezel, A. Kugel, R. M. anner andD. M. Gavrila, (2002) 'FPGA-based Template Matching using Distance Transforms', IEEE symposium on Field-Programmable Custom Computing Machine, Napa, USA.
  15. Nadir Nourain Dawoud, Brahim Belhaouari Samir , Josefina Janier, (2011) "Fast Template Matching Method Based Optimized Sum of Absolute Difference Algorithm for Face Localization", International Journal of Computer Applications (0975 – 8887) Volume 18– No. 8, pp. 30-34.

Keywords

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