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Multiscale corner detection by using wavelet transform.

J S Lee1, Y N Sun, C H Chen

  • 1Dept. of Electr. Eng., Nat. Cheng Kung Univ., Tainan.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|January 1, 1995
PubMed
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This study introduces a novel multiscale corner detection algorithm using wavelet transforms. The new method effectively distinguishes corners from arcs and is robust to noise, outperforming existing detectors.

Area of Science:

  • Computer Vision
  • Image Processing
  • Signal Processing

Background:

  • Corner detection is crucial for image analysis.
  • Existing methods struggle with noise and distinguishing corners from arcs.
  • Multiscale approaches offer improved robustness but can be computationally intensive.

Purpose of the Study:

  • To propose a novel multiscale corner detection algorithm.
  • To enhance the accuracy and efficiency of corner detection.
  • To effectively differentiate between corners and arcs in images.

Main Methods:

  • Utilizes wavelet transform of contour orientation.
  • Employs local extrema and modulus of transform results.
  • Calculates ramp-width of contour orientation profile across two scales.

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Main Results:

  • The proposed detector effectively identifies corners and arcs.
  • Demonstrates superior performance compared to single- and multiple-scale detectors.
  • Exhibits insensitivity to boundary noise.
  • Offers improved efficiency due to fewer scales and fast transform implementation.

Conclusions:

  • The multiscale corner detection algorithm provides accurate and efficient results.
  • The method's robustness to noise and ability to distinguish corners from arcs are significant advantages.
  • This approach represents an advancement in corner detection techniques.