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Multisensor image registration via implicit similarity.

Yosi Keller1, Amir Averbuch

  • 1Department of Mathematics, Yale University, New Haven, CT 06520-8283, USA. yosi.keller@yale.edu

IEEE Transactions on Pattern Analysis and Machine Intelligence
|April 28, 2006
PubMed
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This study introduces a novel image registration method for dissimilar, multi-sensor images. It aligns gradient maxima to robustly match images without direct intensity comparison, even with quality differences.

Area of Science:

  • Computer Vision
  • Image Processing
  • Remote Sensing

Background:

  • Image registration is crucial for fusing multi-sensor data.
  • Traditional methods struggle with significantly dissimilar images and varying modalities.
  • Robust alignment is needed for accurate analysis of multisensor datasets.

Purpose of the Study:

  • To develop a robust image registration approach for dissimilar images from different sensors.
  • To enable accurate alignment without relying on direct intensity similarity measures.
  • To handle complex intensity variations and differing image qualities.

Main Methods:

  • Aligning gradient maxima locations between images.
  • Iteratively maximizing gradient magnitudes using a coarse-to-fine strategy.

Related Experiment Videos

  • Employing a directional similarity measure for false matching detection and weighting.

    • Main Results:

    • Achieved accurate registration of significantly dissimilar multisensor images.
    • Successfully estimated affine and projective motions despite complex intensity transformations.
    • Demonstrated robustness even when one image has lower quality (noise, blur).

    Conclusions:

    • The proposed gradient maxima alignment method offers a robust solution for multisensor image registration.
    • The approach is effective for images with substantial visual differences and varying quality.
    • This technique enhances the reliability of multisensor data fusion and analysis.