Abstract
Finding lineal features in an image is an important step in many object recognition and scene analysis procedures. Previous feature extraction algorithms exhibit poor parallel performance because features often extend across large areas of the data set. This paper describes a parallel method for extracting lineal features based on an earlier sequential algorithm, stick growing. The new method produces results qualitatively similar to the sequential method.
Experimental results show a significant parallel processing speed-up attributable to three key features of the method: a large numbers of lock preemptible search jobs, a random priority assignment to source search regions, and an aggressive deadlock detection and resolution algorithm. This paper also describes a portable generalized thread model. The model supports a light-weight job abstraction that greatly simplifies parallel vision programming.
Galen Hunt was supported by a research fellowship from Microsoft Corporation.
Randal Nelson was supported in part by ONR grant number N00014-93-1-0221.
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References
J. B. Burns, A. R. Hanson, and E. M. Riseman. Extracting Straight Lines. In Proc. DARPA IU Workshop, pages 165–168, New Orleans, LA, 1984.
R. O. Duda and P. E. Hart. Use of the Hough Transform to Detect Lines and Curves in Pictures. Communications of the ACM, 15:11–15, 1972.
D. Gerogiannus and S. C. Orphanoudakis. Load balancing requirement in parallel implementations of image feature extraction tasks. IEEE Transactions on Parallel and Distributed Systems, 4(9):994–1013, 1993.
T. H. Hong, M. O. Shneier, R. L. Hartley, and A. Rosenfeld. Using pyramids to detect good continuation. IEEE Transactions on Systems, Man and Cybernetics, 13:631–635, 1983.
C.-C. Lin, V. K. Prasanna, and A. Khokhar. Scalable Parallel Extraction of Linear Features on MP-2. In M. A. Bayoumi, L. S. Davis, and K. P. Valavanis, editors, Proceedings of the IEEE Workshop on Computer Architectures for Machine Perception, pages 352–361, New Orleans, LA, 1993.
J. J. Little. Parallel Algorithms for Computer Vision on the Connection Machine. AIM-928, Massachusetts Institute of Technology, Artificial Intelligence Laboratory, 1986.
D. G. Lowe. Perceptual Organization and Visual Recognition. Kluwer Academic Publishers, Hingham, MA, 1985.
A. Mansouri, A. S. Malowany, and M. D. Levine. Line Detection in Digital Pictures: A Hypothesis Prediction / Verification Paradigm. Computer Vision, Graphics, and Image Processing, 40:95–114, 1987.
V. S. Nalwa and E. Pauchon. Edgel Aggregation and Edge Description. Computer Vision, Graphics, and Image Processing, 40:79–94, 1987.
R.C. Nelson. Finding Line Segments by Stick Growing. IEEE Transactions on Pattern Analysis and Machine Intelligence, 16(5):519–523, 1994.
R. Nevatia and K. R. Babu. Linear Feature Extraction and Description. Computer Vision Graphics and Image Processing, 13:257–269, 1980.
V. K. Prasanna, C.-L. Wang, and A. Khokhar. Low Level Vision Processing on Connection Machine CM-5. In M. A. Bayoumi, L. S. Davis, and K. P. Valavanis, editors, Proceedings of the IEEE Workshop on Computer Architectures for Machine Perception, pages 117–126, New Orleans, LA, 1993.
J. Princen, J. Illingworth, and J. Kittler. A hierarchical approach to line extraction based on the Hough transform. Computer Vision Graphics and Image Processing, 52:57–77, 1990.
C. M. Williams. An Efficient Algorithm for the Piecewise Linear Approximation of Planar Curves. Computer Graphics and Image Processing, 8:286–293, 1978.
Y. T. Zhou, V. Venkateswar, and R. Chellappa. Edge Detection and Linear Feature Extraction Using a 2-D Random Field Model. IEEE Trans. Pattern Analysis and Machine Intelligence, 11:84–95, 1989.
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© 1996 Springer-Verlag Berlin Heidelberg
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Hunt, G.C., Nelson, R.C. (1996). Lineal feature extraction by parallel stick growing. In: Ferreira, A., Rolim, J., Saad, Y., Yang, T. (eds) Parallel Algorithms for Irregularly Structured Problems. IRREGULAR 1996. Lecture Notes in Computer Science, vol 1117. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0030107
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DOI: https://doi.org/10.1007/BFb0030107
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