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Tensor voting for image correction by global and local intensity alignment.

Jiaya Jia1, Chi-Keung Tang

  • 1Department of Computer Science, the Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong. leojia@cs.ust.hk

IEEE Transactions on Pattern Analysis and Machine Intelligence
|January 5, 2005
PubMed
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This study introduces a novel tensor voting method for image correction and mosaicking. The approach robustly estimates intensity alignment functions, enabling effective image enhancement and outlier rejection without complex models.

Area of Science:

  • Computer Vision
  • Image Processing
  • Computational Geometry

Background:

  • Image correction and mosaicking are crucial for various applications.
  • Existing methods often rely on complex models or assumptions about replacement functions.
  • Robust intensity alignment and outlier rejection remain challenging problems.

Purpose of the Study:

  • To present a modeless voting method for image correction and global/local intensity alignment.
  • To develop a robust approach for inferring replacement functions and handling image outliers.
  • To demonstrate applications in image mosaicking, occlusion handling, and high-contrast image correction.

Main Methods:

  • Utilizing 2D tensor voting in dedicated voting spaces to estimate global and local replacement functions.

Related Experiment Videos

  • Employing a dense tensor field to propagate curve smoothness constraints under a monotonic constraint.
  • Applying the voted replacement functions within an iterative registration algorithm for image mosaicking.

    • Main Results:

    • Successfully inferred missing curve segments and rejected image outliers.
    • Achieved effective image mosaicking of static scenes by computing optimal warping matrices.
    • Demonstrated visually acceptable mosaic construction in the presence of occlusion.
    • Performed image intensity compensation and high contrast image correction with defective input images.

    Conclusions:

    • The proposed tensor voting method offers a robust and modeless approach to image correction and alignment.
    • The framework effectively handles challenges like occlusion and defective input images.
    • This technique provides a versatile solution for image mosaicking and enhancement tasks.