Ingenieria

Hough transform algorithm for real-time pattern recognition using an artificial retina camera
Xin Lin and Kazunori Otobe
National Agriculture Research Center, 3-1-1 Kannondai, Tsukuba, 305-8666 Japan xlin@narc.affrc.go.jp http://www.narc.affrc.go.jp/pub/xlin

Abstract: An artificial retina camera (ARC) is employed for real-time preprocessing of images. And the algorithm of Hough transform isadvanced for detecting the biology-images with approximate circle edgeinformation in the two-dimension space. This method also works in parallel for processing multiple input and partial input patterns.
2001 Optical Society of America
OCIS codes: (100.0100) Image processing, (070.5010) Pattern recognition and feature extraction

References and links
1. 2. 3. 4. 5. 6. P. V. C. Hough, “Methodsand means for recognition complex pattern,” U. S. Patent 3,069,654 (1962). D. H. Ballard, “Generalizing the Hough transform to detect arbitrary shapes,” Pattern Recognition, 13(2), 111-122 (1981). D. Casasent and R. Krishnapuram, “Curved object location by Hough transformations and inversions,” Pattern Recognition, 20(2), 181-188 (1987). X. Lin and K. Otobe, “Real-time biology-image recognitionusing an artificial retina LSI,” in 2000 OSA Annual Meeting /ILS-XVI of Proceedings, pp. 111. K. Kyuma, E. Lange, J. Ohta, A. Hermanns, B. Banish, and M. Oita “Artificial retinas – fast, versatile image processors,” Nature, 372(6502), 197-198 (1994). H. Kage, W. T. Freeman, Y. Miyake, E. Funatsu, K. Tanaka, and K. Kyuma, “Artificial retina chips as onchip image processors and gesture-orientedinterfaces,” Opt. Eng. 38(12), 1979-1988 (1999).

1. Introduction The Hough transform (HT) [1] is an effective technique for detecting and finding the images within noise. Specifically, the straight-line detection case has been ingeniously exploited in several applications. However, the HT techniques to recognize other shapes except for the straight-line are quite complex because the points in theinput space will be mapped to hypersurfaces and thus, the Hough space will become high dimensional. So that the calculation and memory cost is very high. A number of improved methods have been proposed to detect arbitrary shapes [2,3]. In fact, in an image, the pertinent information is very often contained in the shape of its boundary. The crude encoding of the boundary is sufficient for imagerecognition, i.e. an image may be initially encoded as an edge-image. In this paper, we describe an algorithm of modificatory HT for detecting the biology-images using its edge-information only in the twodimension (no hypersurfaces). On the other hand, an artificial retina camera (ARC) is employed for edge-extraction processing in real-time. This method can work to process multiple and partial inputpatterns [4]. 2. Principles of the method 2.1 Modificatory Hough transform for detecting circle-edge image The straight-line HT maps point to sinusoids as shown in Fig.1(b). If there a peak point in Hough space and it is the cumulative value of all the sinusoids, then a straight-line can be

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Received February 20, 2001, Revised April 16, 2001

(C) 2001 OSA

23 April 2001 /Vol. 8, No. 9 / OPTICS EXPRESS 503

defined in the input space, as shown in Fig. 1(a). We can see that the Hough space only is a simple two-dimension (2-D) space. In order to reduce the dimensionality in the Hough space when detecting a circle object, we describe an algorithm for detecting circle objects from an image that has been transformed into such an edge representation, and then, withthe help of the concept of straight-line HT to reach detection circle patterns only in 2-D space. The principles of this method are described as follows. Any image of edge representation can be considered to be composed small, tangential, straight-line segments. So we can try detecting a circle-edge image by the straight-line HT. Each of the short tangential-line segments in the input space maps...