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Surface dependent representations for illumination insensitive image comparison.

Margarita Osadchy1, David W Jacobs, Michael Lindenbaum

  • 1Computer Science Department, University of Haifa, Mount Carmel, Haifa 31905, Israel. rita@cs.haifa.ac.il

IEEE Transactions on Pattern Analysis and Machine Intelligence
|November 17, 2006
PubMed
Summary
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Image matching under varying lighting conditions depends on surface properties. A new mixed strategy combining different image comparison methods is proposed for robust object recognition.

Area of Science:

  • Computer Vision
  • Image Processing
  • Machine Learning

Background:

  • Matching images acquired under different lighting conditions is a key challenge in computer vision.
  • Existing methods often focus on specific surface properties, limiting their general applicability.

Purpose of the Study:

  • To investigate how surface characteristics influence the choice of image comparison techniques.
  • To develop a robust image matching strategy for objects under varying illumination.

Main Methods:

  • Analytical comparison of established image comparison methods (gradient direction, normalized correlation, multiscale filters).
  • Evaluation of whitening filters for surfaces with slowly changing properties.
  • Development and validation of a novel mixed strategy combining different approaches.

Related Experiment Videos

Main Results:

  • Surface characteristics dictate the optimal image comparison method.
  • Normalized correlation and multiscale oriented filters compute similar information.
  • Whitening filters are effective for surfaces with gradual property changes.

Conclusions:

  • A combination of image comparison strategies is necessary for general object matching under varying lighting.
  • The proposed mixed strategy demonstrates effectiveness on both synthetic and real-world image data.