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Stereoscopic ranging by matching image modulations.

T Y Chen1, A C Bovik, L K Cormack

  • 1Institute of Science and Technology, Taiwan, ROC.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|February 13, 2008
PubMed
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This study introduces a novel stereo matching algorithm using AM-FM surface albedo models and Gabor filters. It accurately computes dense disparity maps, overcoming phase wrapping issues common in high-frequency image data.

Area of Science:

  • Computer Vision
  • Image Processing
  • Photogrammetry

Background:

  • Stereo matching algorithms compute depth from binocular images.
  • Local image phase is a useful feature for stereo matching.
  • Phase wrapping at high frequencies limits disparity accuracy.

Purpose of the Study:

  • To develop a robust stereo matching algorithm using AM-FM models.
  • To overcome phase wrapping limitations in disparity computation.
  • To generate dense and accurate disparity maps.

Main Methods:

  • Application of an AM-FM surface albedo model.
  • Utilizing multichannel Gabor image demodulation for multiscale phase computation.
  • Integration with a disparity channel model for depth calculation.

Related Experiment Videos

Main Results:

  • A unified framework for phase-based matching and AM-FM models.
  • Demonstration of a stereo algorithm generating dense disparity maps.
  • Successful mitigation of phase wrapping problems.

Conclusions:

  • The proposed method offers accurate dense disparity mapping.
  • The framework effectively combines AM-FM models with phase-based stereo matching.
  • This approach enhances stereo vision capabilities by resolving high-frequency challenges.