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Related Experiment Video

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Matching by Monotonic Tone Mapping.

Gyorgy Kovacs

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |June 17, 2017
    PubMed
    Summary

    A new Matching by Monotonic Tone Mapping (MMTM) technique offers efficient image matching under non-linear intensity changes. This novel dissimilarity measure proves competitive for template matching with monotonic tone mappings.

    Area of Science:

    • Computer Vision
    • Image Processing
    • Pattern Recognition

    Background:

    • Template matching is crucial for image analysis.
    • Existing methods struggle with non-linear monotonic intensity transformations.
    • Need for robust dissimilarity measures invariant to tone mapping.

    Purpose of the Study:

    • Propose a novel dissimilarity measure, Matching by Monotonic Tone Mapping (MMTM).
    • Evaluate MMTM's efficiency and performance under monotonic tone mappings.
    • Compare MMTM against existing invariant measures.

    Main Methods:

    • Developed the MMTM technique for matching under non-linear monotonic tone mappings.
    • Approximated tone mappings using piecewise constant or linear functions for efficient computation.

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  • Validated MMTM in template matching scenarios with simulated and real images.

    • Main Results:

    • MMTM demonstrated efficient computation when tone mappings are approximated.
    • The proposed measure showed high competitiveness against conventional methods.
    • MMTM performed effectively in template matching tasks with monotonic intensity variations.

    Conclusions:

    • MMTM is a viable and efficient alternative for image matching under monotonic transformations.
    • The method offers robustness in scenarios with non-linear intensity changes.
    • MMTM advances the field of invariant template matching.