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Image registration under illumination variations using region-based confidence weighted M-estimators.

Mohamed M Fouad1, Richard M Dansereau, Anthony D Whitehead

  • 1Department of Company Engineering, Military Technical College, Cairo, Egypt. mmafoad@ieee.org

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|September 13, 2011
PubMed
Summary

This study introduces a novel image registration model that effectively handles challenging local illumination variations. The new method significantly improves subpixel registration precision by robustly compensating for arbitrary illumination changes.

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Area of Science:

  • Computer Vision
  • Image Processing
  • Computational Imaging

Background:

  • Non-geometric variations, such as local illumination changes, degrade image registration precision.
  • Traditional methods fail to account for arbitrary illumination shifts, impacting subsequent analyses.
  • Least-square estimators are sensitive to outliers, common in significant interimage illumination differences.

Purpose of the Study:

  • To develop an image registration approach robust to arbitrarily shaped local illumination variations.
  • To enhance geometric registration precision in image sets with significant interimage illumination differences.
  • To improve the reliability of image registration when illumination conditions change unpredictably.

Main Methods:

  • Proposed a robust M-estimator approach tailored for regional illumination variations.

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  • Implemented distinct cost functions for M-estimators to penalize residuals unevenly.
  • Incorporated segmentation confidence weighting to mitigate mis-segmentation effects at boundaries.
  • Utilized an iterative coarse-to-fine framework for efficient convergence.

    • Main Results:

    • Achieved nearly tenfold improvement in subpixel registration precision compared to existing methods.
    • Demonstrated superior performance on both simulated and real-world datasets with illumination variations.
    • The proposed method shows convergence rates comparable to traditional intensity-based registration.

    Conclusions:

    • The novel image registration model effectively compensates for arbitrary illumination variations.
    • The approach significantly enhances registration accuracy, particularly in challenging lighting conditions.
    • This method offers a robust solution for applications requiring precise geometric alignment despite illumination changes.