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Adaptive Curved Feature Detection Based on Ridgelet

  • Kang Liu
  • Licheng Jiao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3211)

Abstract

Feature detection always is an important problem in image processing. Ridgelet performs very well for objects with linear singularities. Based on the idea of ridgelet, this paper presents an adaptive algorithm for detecting curved feature in anisotropic images. The curve is adaptively partitioned into fragments with different length, and these fragments are nearly straight at fine scales, then it can be detected by using ridgelet transform. Experimental results prove the efficiency of this algorithm.

Keywords

Adaptive Algorithm Curve Singularity Speckle Noise Curve Feature Break Part 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Kang Liu
    • 1
  • Licheng Jiao
    • 1
  1. 1.National Key Lab for Radar Signal Processing, and Institute of Intelligent Information ProcessingXidian UniversityXi’anChina

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