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International Workshop on Parallel Algorithms for Irregularly Structured Problems

IRREGULAR 1996: Parallel Algorithms for Irregularly Structured Problems pp 171–182Cite as

Lineal feature extraction by parallel stick growing

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1117))

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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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Rochester, 14627, Rochester, NY, USA

    Galen C. Hunt & Randal C. Nelson

Authors
  1. Galen C. Hunt
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  2. Randal C. Nelson
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Editor information

Alfonso Ferreira José Rolim Yousef Saad Tao Yang

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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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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-61549-1

  • Online ISBN: 978-3-540-68808-2

  • eBook Packages: Springer Book Archive

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