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IJMLC 2011 Vol.1(4): 400-404 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2011.V1.59

The Comparison of Object Recognition and Identification by Using Image Processing Base on the Neural Network, the Hough transform and the Harris Corner Detection

Jaruwan Toontham and Chaiyapon Thongchaisuratkrul

Abstract—This paper presents an object recognition and identification system using the back propagation neural networks. The performance of Hough Transform and the Harris Corner detection are compared with the following procedures and methods; the webcam is used to capture the object and create an input image, change the color image from RGB to gray scale, resize, learn and recognize the objects by neural network, and separate the objects by the robot arm. Three different types of objects in this study are triangle, rectangle and rigid circle. The object recognition and identification from the neural network, the Hough transform, and the Harris corner detection are compared. The results showed that the neural network gives more accuracy than the Hough transform and the Harris corner detection.

Index Terms—Hough Transform, Harris Corner Detection, Back Propagation Neural Network

Jaruwan toontham is with King Mongkut's University of Technology North Bangkok, Bangkok,Thailand.( e-mail: nudiddl@hotmail.com)
Chaiyapon Thongchaisuratkul is with the King Mongkut's University of Technology North Bangkok, Thailand. (e-mail: srp@kmutnb.ac.th)

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Cite: Jaruwan Toontham, Chaiyapon Thongchaisuratkrul, "The Comparison of Object Recognition and Identification by Using Image Processing Base on the Neural Network, the Hough transform and the Harris Corner Detection," International Journal of Machine Learning and Computing vol. 1, no. 4, pp.400-404, 2011.

General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
  • E-mail: ijmlc@ejournal.net


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