A Swift and Memory Efficient Hough Transform for Systems with Limited Fast Memory

This paper focuses on implementation of a speedy Hough Transform (HT) which considers the memory constraints of the system. Because of high memory demand, small systems (DSPs, tiny robots) cannot realize efficient implementation of HT. Keeping this scenario in mind, the paper discusses an effective and memory-efficient method of employing the HT for extraction of line features from a gray scale image. We demonstrate the use of a circular buffer for extraction of image edge pixels and store the edge image in a manner that is different from the conventional way. Approximation of the two dimensional Hough Space by a one dimensional array is also discussed. The experimental results reveal that the proposed algorithm produces better results, on small and large systems, at a rapid pace and is economical in terms of memory usage.

[1]  Yi Pan,et al.  An improved constant-time algorithm for computing the Radon and Hough transforms on a reconfigurable mesh , 1999, IEEE Trans. Syst. Man Cybern. Part A.

[2]  Myung Hwangbo,et al.  Visual self-localization for indoor mobile robots using natural lines , 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).

[3]  Allen R. Hanson,et al.  Extracting Straight Lines , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  R. Singleton,et al.  A method for computing the fast Fourier transform with auxiliary memory and limited high-speed storage , 1967, IEEE Transactions on Audio and Electroacoustics.

[5]  John Immerkær.  Some remarks on the straight line Hough transform , 1998, Pattern Recognit. Lett..

[6]  Michael F. P. O'Boyle,et al.  Compiler Reduction of Invalidation Traffic in Virtual Shared Memory Systems , 1996, Euro-Par, Vol. I.

[7]  Sharlee Climer,et al.  Local Lines: A linear time line detector , 2003, Pattern Recognit. Lett..

[8]  Nikos Papamarkos,et al.  Accelerated Hough transform using rectangular image decomposition , 1996 .

[9]  P. Nagabhushan,et al.  A simple and robust line detection algorithm based on small eigenvalue analysis , 2004, Pattern Recognit. Lett..

[10]  Muhammad Usman Karim Khan,et al.  Memory Efficient Vision Based Line Feature Extraction for Tiny Mobile Robots , 2009, ICIAR.

[11]  Heikki Kälviäinen,et al.  An extension to the randomized hough transform exploiting connectivity , 1997, Pattern Recognit. Lett..

[12]  Richard O. Duda,et al.  Use of the Hough transformation to detect lines and curves in pictures , 1972, CACM.

[13]  S. Mahlknecht,et al.  TINYPHOON A Tiny Autonomous Mobile Robot , 2005, Proceedings of the IEEE International Symposium on Industrial Electronics, 2005. ISIE 2005..

[14]  Emilio L. Zapata,et al.  A Parallel Pipelined Hough Transform , 1996, Euro-Par, Vol. II.

[15]  Erkki Oja,et al.  Probabilistic and non-probabilistic Hough transforms: overview and comparisons , 1995, Image Vis. Comput..