single-jc.php

JACIII Vol.13 No.4 pp. 380-385
doi: 10.20965/jaciii.2009.p0380
(2009)

Paper:

Proposal of a Method to Extract Arbitrary FiguresUsing One-Dimensional Histograms

Shota Nakashima*, Makoto Miyauchi**, and Seiichi Serikawa*

*Department of Electrical Engineering, Kyushu Institute of Technology, Kitakyushu, Japan

**Department of Integrated Arts and Sciences, Kitakyushu National College of Technology, Japan

Received:
November 25, 2008
Accepted:
March 2, 2009
Published:
July 20, 2009
Keywords:
image processing, polytope method, one-dimensional histogram, generalized hough transform
Abstract

Arbitrary figure extraction, a basic image processing problem, is done typically using the generalized Hough transform (GHT). GHT and its successors tend, however, to consume humongous amounts of processing time and memory space. The arbitrary figure extraction we propose using one-dimensional histograms takes advantage of the Polytope method, which features: (1) The histogram distribution changes if parameters representing figures change. (2) Optimum parameters are obtained, if the value of the highest-frequency histogram becomes maximum. This approach makes memory space very small, processing time very short, effective by extracts arbitrary curves with different aspect ratios, and the algorithm is simple.

Cite this article as:
Shota Nakashima, Makoto Miyauchi, , and Seiichi Serikawa, “Proposal of a Method to Extract Arbitrary FiguresUsing One-Dimensional Histograms,” J. Adv. Comput. Intell. Intell. Inform., Vol.13, No.4, pp. 380-385, 2009.
Data files:
References
  1. [1] M. Saji, A. Ueno, H. Takeda, “A Robot System to Recognize and Manage Unfixable Objects in the Office Environment,” IEICE technical report, Vol.99, No.717, pp. 1-8, 2000.
  2. [2] D.H. Ballard, “Generalizing the Hough transform to detect arbitrary shapes,” Pattern Recognition, Vol.13, No.2, pp. 111-122, 1981.
  3. [3] R.O. Duda and P.E. Hart, “Use of the Hough transformation to detect lines and curves in pictures,” Commun. ACM, Vol.15, No.1, pp. 11-15, 1972.
  4. [4] P.-F. Fung, W.-S. Lee, and I. King, “Randomized generalized Hough transform for 2-D gray scale object detection,” Pattern Recognition, Vol.2, pp. 511-515, 1996.
  5. [5] C. F. Olson, “Improving the generalized Hough transform through imperfect grouping,” Image and Vision Computing, Vol.16, No.9-10, pp. 627-634, 1998.
  6. [6] A. Kimura and T. Watanabe, “Generalized Hough Transform to be Extended as an Affine-Invariant Detector Arbitrary Shapes,” IEICE, Vol.J84-D-II, No.5, pp. 789-798, 2001.
  7. [7] T. Miyatake, T. Matsuyama, and M. Nakao, “Affine Transform Invariant Curve Recognition Using Fourier Descriptors,” IPSJ, Vol.24, No.1, pp. 64-71, 1983.
  8. [8] T. Nagao, T. Agui, and H. Nagahashi, “Pattern Matching of Binary Shapes Using a Genetic Method,” IEICE, Vol.J76-D-II, No.3, pp. 557-565, 1993.
  9. [9] F.C.D. Tsai, “Geometric hashing with line features,” Pattern Recognit., Vol.27, No.3, pp. 377-389, 1994.
  10. [10] H. Okumura, “An algorithm encyclopedia using C,” Gijutsu-Hyohron Co., p. 262, 2006.
  11. [11] W.H. Press, B.P. Flannery, S. A. Teulolsky, and W. T. Vetterling, “Numerical Recipes-The Art of Scientific Computing,” Cambridge University Press, pp. 289-293, 1987.
  12. [12] A. Kimura and T. Watanabe, “Fast Generalized Hough Transform that Improves its Robustness of Shape Detection,” IEICE, Vol.J83-D-II, No.5 pp. 1256-1265, 2000.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on May. 04, 2021