Lineal Feature Extraction by Parallel Stick Growing.

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.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 1996
Accession Number
ADA329864

Entities

People

  • Galen C. Hunt
  • Randal C. Nelson

Organizations

  • University of Rochester

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Abstracts
  • Algorithms
  • Computations
  • Computer Programming
  • Computer Programs
  • Computer Science
  • Computer Vision
  • Computers
  • Detection
  • Extraction
  • Feature Extraction
  • Image Processing
  • Object Recognition
  • Operating Systems
  • Parallel Computing
  • Parallel Processing
  • Preprocessing

Fields of Study

  • Computer science

Readers

  • Computer Vision.
  • Parallel and Distributed Computing.

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms