Ziv Gigus and Jitendra Malik

EECS Department, University of California, Berkeley

Technical Report No. UCB/CSD-91-619

, 1991

http://www2.eecs.berkeley.edu/Pubs/TechRpts/1991/CSD-91-619.pdf

Humans have a well developed ability to detect curvilinear structure in noisy images. Good algorithms for performing this process would be very useful in machine vision for image segmentation and object recognition. Previous approaches to this problem such as those due to Parent and Zucker and Sha'shua and Ullman have been based on relaxation. We have developed a simple feedforward and parallel approach to this problem based on the idea of developing filters tuned to local oriented circular arcs. This provides a natural second order generalization of the idea of directional operators popular for edge detection. Curve detection can then be done by methods very similar to those used for edge detection. Experimental results are shown on both synthetic and natural images. We also review data from an experiment investigating human preattentive line segregation and present predictions from our model that agree with this data. (February 1991, Revised April 1991)

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BibTeX citation:

@techreport{Gigus:CSD-91-619,
    Author= {Gigus, Ziv and Malik, Jitendra},
    Title= {Detecting Curvilinear Structure in Images},
    Year= {1991},
    Month= {Apr},
    Url= {http://www2.eecs.berkeley.edu/Pubs/TechRpts/1991/5344.html},
    Number= {UCB/CSD-91-619},
    Abstract= {Humans have a well developed ability to detect curvilinear structure in noisy images. Good algorithms for performing this process would be very useful in machine vision for image segmentation and object recognition. Previous approaches to this problem such as those due to Parent and Zucker and Sha'shua and Ullman have been based on relaxation. We have developed a simple feedforward and parallel approach to this problem based on the idea of developing filters tuned to local oriented circular arcs. This provides a natural second order generalization of the idea of directional operators popular for edge detection.  Curve detection can then be done by methods very similar to those used for edge detection. Experimental results are shown on both synthetic and natural images. We also review data from an experiment investigating human preattentive line segregation and present predictions from our model that agree with this data. (February 1991, Revised April 1991)},
}

EndNote citation:

%0 Report
%A Gigus, Ziv 
%A Malik, Jitendra 
%T Detecting Curvilinear Structure in Images
%I EECS Department, University of California, Berkeley
%D 1991
%@ UCB/CSD-91-619
%U http://www2.eecs.berkeley.edu/Pubs/TechRpts/1991/5344.html
%F Gigus:CSD-91-619