Detection Of Oriented Line Segments Using Discrete Cosine Transform

H. S. Hou; M. J. Vogel

doi:10.1117/12.960262

27 March 1989 Detection Of Oriented Line Segments Using Discrete Cosine Transform

H. S. Hou, M. J. Vogel

Proceedings Volume 1002, Intelligent Robots and Computer Vision VII; (1989) https://doi.org/10.1117/12.960262
Event: 1988 Cambridge Symposium on Advances in Intelligent Robotics Systems, 1988, Boston, MA, United States

Abstract

The widespread use and increasing sophistication of computer vision systems has generated a great deal of interest in image processing techniques capable of tracking moving targets at high speed. The Hough transform has been cohnonly used as a means of detecting target trajectories. Nevertheless, the computational load for performing a Hough transform is overwhelming. In addition, the re$ult is usually noisy. In a recent study, we discovered that the discrete cosine transform (DCT) behaves like a directional filter and, as such, can be used to detect and locate oriented line segments. This paper describes the directional filter property of the DCT and the fast computing algorithm for detecting oriented lines. The DCT has been used in image data compression for many years. Many fast DCT algorithms exist, and VLSI chips for implementing such algorithms have been designed. In this paper the directional filter property of DCT is first presented. The one-dimensional. DCT, being viewed as a filter, can split the filtered output into two halves, one corresponding to the low-frequency band of the input and the other to the high-frequency band of the input. In the two-dimensional case, the DCT behaves like a quadrature filter without aliasing. Based on the quadrature filter property of DCT, a new method for detecting oriented line segments has emerged. We next present the fast processing algorithm for digital implementation and conclude with some simulation results.

Citation Download Citation

H. S. Hou and M. J. Vogel "Detection Of Oriented Line Segments Using Discrete Cosine Transform", Proc. SPIE 1002, Intelligent Robots and Computer Vision VII, (27 March 1989); https://doi.org/10.1117/12.960262

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