Subpixel Detection of Circular Objects Using Geometric Property
In this paper, we propose a method for detecting
circular shapes with subpixel accuracy. First, the geometric properties
of circles have been used to find the diameters as well as the
circumference pixels. The center and radius are then estimated by the
circumference pixels. Both synthetic and real images have been tested
by the proposed method. The experimental results show that the new
method is efficient.
[1] D. H. Ballard, Generalizing the Hough transform to detect arbitrary
shapes, Pattern Recognition, 13, 1981, 111-122.
[2] M. Berman, Large sample bias in least squares estimators of a circular arc
center and its radius, Computer Vision, Graphics, and Image Processing,
45, 1989, 126-128.
[3] R. Chan and W. C. Siu, New parallel Hough transformation for circles,
IEE Proceedings-E, 138, 1991, 335-344.
[4] F. L. Chen and S. W. Lin, Subpixel estimation of circle parameters using
orthogonal circular detector, Computer Vision and Image Understanding,
78, 2000, 206-221.
[5] L. H. Chen and K. L. Lee, A new method for circular object detection and
location, Pattern Recognition Letters, 11, 1990, 691-697.
[6] R. S. Conker, A dual plane variation of the Hough transform for detecting
non-concentric circles of different radii, Computer Vision, Graphics, and
Image Processing, 43, 1988, 115-132.
[7] E. R. Davies, A high speed algorithm for circular object location, Pattern
Recognition Letters. 6, 1987, 323-333.
[8] E. R. Davies, A modified Hough scheme for general circle location,
Pattern Recognition Letters. 7, 1988, 37-43.
[9] E. R. Davies, A hybrid sequential-parallel approach to accurate circle
center location, Pattern Recognition Letters. 7, 1988, 279-290.
[10] R. O. Duda and P. E. Hart, Use of Hough transformation to detect lines
and curves in pictures, Communications of the ACM. 15, 1972, 11-15.
[11] J. Y. Goulermas and P. Liatsis, Genetically fine-tuning the Hough
transform feature space, for the detection of circular objects, Image and
Vision Computing, 16, 1998, 615-625.
[12] N. Guil and E. L. Zapata, Lower order circle and ellipse Hough transform,
Pattern Recognition, 30, 1997, 1929-1744.
[13] D. Ioannou, W. Huda, and A. F. Laine, Circle recognition through a 2D
Hough transformation and radius histogramming, Image and Vision
Computing, 17, 1999, 15-26.
[14] A. Kavianpour and N. Bagherzadeh, Finding circular shapes in an image
on a pyramid architecture, Pattern Recognition Letters, 13, 1992,
843-848.
[15] P. Kierkegaard, A method for detection of circular arcs based on the
Hough transform, Machine and Vision Applications, 5, 1992, 249-263.
[16] G. Kimme, D. Ballard, and J. Sklansky, Finding circles by an array of
accumulators, Communications of the ACM. 18, 1975, 120-122.
[1] D. H. Ballard, Generalizing the Hough transform to detect arbitrary
shapes, Pattern Recognition, 13, 1981, 111-122.
[2] M. Berman, Large sample bias in least squares estimators of a circular arc
center and its radius, Computer Vision, Graphics, and Image Processing,
45, 1989, 126-128.
[3] R. Chan and W. C. Siu, New parallel Hough transformation for circles,
IEE Proceedings-E, 138, 1991, 335-344.
[4] F. L. Chen and S. W. Lin, Subpixel estimation of circle parameters using
orthogonal circular detector, Computer Vision and Image Understanding,
78, 2000, 206-221.
[5] L. H. Chen and K. L. Lee, A new method for circular object detection and
location, Pattern Recognition Letters, 11, 1990, 691-697.
[6] R. S. Conker, A dual plane variation of the Hough transform for detecting
non-concentric circles of different radii, Computer Vision, Graphics, and
Image Processing, 43, 1988, 115-132.
[7] E. R. Davies, A high speed algorithm for circular object location, Pattern
Recognition Letters. 6, 1987, 323-333.
[8] E. R. Davies, A modified Hough scheme for general circle location,
Pattern Recognition Letters. 7, 1988, 37-43.
[9] E. R. Davies, A hybrid sequential-parallel approach to accurate circle
center location, Pattern Recognition Letters. 7, 1988, 279-290.
[10] R. O. Duda and P. E. Hart, Use of Hough transformation to detect lines
and curves in pictures, Communications of the ACM. 15, 1972, 11-15.
[11] J. Y. Goulermas and P. Liatsis, Genetically fine-tuning the Hough
transform feature space, for the detection of circular objects, Image and
Vision Computing, 16, 1998, 615-625.
[12] N. Guil and E. L. Zapata, Lower order circle and ellipse Hough transform,
Pattern Recognition, 30, 1997, 1929-1744.
[13] D. Ioannou, W. Huda, and A. F. Laine, Circle recognition through a 2D
Hough transformation and radius histogramming, Image and Vision
Computing, 17, 1999, 15-26.
[14] A. Kavianpour and N. Bagherzadeh, Finding circular shapes in an image
on a pyramid architecture, Pattern Recognition Letters, 13, 1992,
843-848.
[15] P. Kierkegaard, A method for detection of circular arcs based on the
Hough transform, Machine and Vision Applications, 5, 1992, 249-263.
[16] G. Kimme, D. Ballard, and J. Sklansky, Finding circles by an array of
accumulators, Communications of the ACM. 18, 1975, 120-122.
@article{"International Journal of Engineering, Mathematical and Physical Sciences:64556", author = "Wen-Yen Wu and Wen-Bin Yu", title = "Subpixel Detection of Circular Objects Using Geometric Property", abstract = "In this paper, we propose a method for detecting
circular shapes with subpixel accuracy. First, the geometric properties
of circles have been used to find the diameters as well as the
circumference pixels. The center and radius are then estimated by the
circumference pixels. Both synthetic and real images have been tested
by the proposed method. The experimental results show that the new
method is efficient.", keywords = "Subpixel, least squares estimation, circle detection,
Hough transformation.", volume = "3", number = "8", pages = "598-5", }
