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Effective multiresolution arc segmentation: algorithms and performance evaluation.

Jiqiang Song1, Michael R Lyu, Shijie Cai

  • 1Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong SAR, P.R. China. songjq@ieee.org

IEEE Transactions on Pattern Analysis and Machine Intelligence
|November 4, 2004
PubMed
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This study introduces a multiresolution arc segmentation method for graphics recognition. The new method significantly improves the processing of large arcs and circles in scanned images.

Area of Science:

  • Computer Vision
  • Image Processing
  • Pattern Recognition

Background:

  • Arc segmentation is crucial for graphics recognition in scanned images.
  • Existing methods have limitations, particularly with large arcs and circles, as highlighted by the GREC contest.

Purpose of the Study:

  • To propose a novel multiresolution arc segmentation method.
  • To enhance the accuracy and efficiency of arc detection and localization.
  • To improve robustness in handling complex image scenarios.

Main Methods:

  • A multiresolution approach building upon a seeded circular tracking algorithm.
  • New techniques for arc seed detection, localization, and verification.
  • Improvements to the dynamic adjustment algorithm for circular tracking.

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Main Results:

  • The proposed method demonstrates stable performance across various conditions (arc angles, lengths, thickness, noise, intersections).
  • Significant improvement in processing large arcs and circles.
  • Experimental results and time complexity analyses show competitive performance compared to other approaches.

Conclusions:

  • The multiresolution arc segmentation method offers a robust and efficient solution for graphics recognition.
  • The approach effectively addresses limitations of previous methods, especially for large-radius arcs and circles.