Detecting Circles in Image Using Statistical Image Analysis

Authors: Fathi M. O. Hamed, Salma F. Elkofhaifee

Abstract:

The aim of this work is to detect geometrical shape objects in an image. In this paper, the object is considered to be as a circle shape. The identification requires find three characteristics, which are number, size, and location of the object. To achieve the goal of this work, this paper presents an algorithm that combines from some of statistical approaches and image analysis techniques. This algorithm has been implemented to arrive at the major objectives in this paper. The algorithm has been evaluated by using simulated data, and yields good results, and then it has been applied to real data.

Keywords: Image processing, median filter, projection, scalespace, segmentation, threshold.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1110335

References:

[1] S. Geman and D. Geman, “Statistical analysis of dirty pictures,” in Advances in Applied Statistics, vol. 20, K.V. Mardia and G.K. Kanji, Ed. Leeds: UK, 1993, pp. 63–87.
[2] J. Besag, “On the statistical analysis of dirty pictures (with discussion),” The Royal Statistical Society J., vol. 48, pp. 259–302, 1986.
[3] B. Jahne, Digital image processing. New York: Springer, 2003.
[4] C. A. Glasbey and G. W. Horgan, Image analysis for the biological sciences. Chichester: Wiley and Sons, 1995.
[5] A. P. Witkin, “Scale-Space filtering,” in proc. 8th international joint Conf. on Artificial intelligence, Germany, 1983, pp. 1019-1022.
[6] J. S. Marron and P. Chaudhur, “Scale-Space view of curve estimation,” The Annals of Statistics, vol. 28, pp. 408-428, 2000.
[7] F. M. Hamed, “Geometrical modeling and identification of structure in image analysis,” phD thesis, University of Leeds, 2005.
[8] M. J. Gangeh, R. P. W. Duin, C. Eswaran and B. M. Rommeny, “Scale Space Texture Classification Using Combined Classifiers with Application to Ultrasound Tissue Characterization,” in proc. Intern. Conf. on Biomedical Engineering, 2006.
[9] J. K. Tukey, “Nonlinear (nonsuperposable) methods for smoothing data,” in proc. Congr. Rec. EASCOM, 1974, pp. 673-681.
[10] J. Lianghai and L. Dehua, “A switching vector median filter based on the CIELAB color space for color image restoration,” Signal Processing, vol. 87, pp. 1345-1354, 2007.
[11] H. G. Senel, R. A. Peters and B. Dawant, “Topological Median Filters,” in IEEE Transactions on Image processing, vol. 11, pp. 89-104, 2002.
[12] T. Lindeberg, “Scale-Space theory: A basic tool for analyzing structures at different scales,” Statistics and Images, vol. 21, pp. 225-270, 1994.