Detecting Circles in Image Using Statistical Image Analysis
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.
[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.
[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.
@article{"International Journal of Information, Control and Computer Sciences:71542", author = "Fathi M. O. Hamed and Salma F. Elkofhaifee", title = "Detecting Circles in Image Using Statistical Image Analysis", 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.", volume = "9", number = "11", pages = "2348-9", }