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Related Experiment Video

Updated: Dec 1, 2025

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
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Blur-Invariant Similarity Measurement of Images.

Matej Lebl, Filip Sroubek, Jan Flusser

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |November 6, 2020
    PubMed
    Summary

    This paper corrects errors in a previously proposed blur-invariant distance measure for images. Experimental comparisons show the corrected method offers improved accuracy for image analysis.

    Area of Science:

    • Computer Vision
    • Image Processing
    • Pattern Recognition

    Background:

    • A novel blur-invariant distance measure was introduced by Gopalan et al. in TPAMI (2012).
    • This measure aims to quantify image similarity despite varying levels of blur.
    • Assessing image similarity is crucial in various computer vision applications.

    Purpose of the Study:

    • To identify and rectify theoretical inaccuracies in the Gopalan et al. (2012) blur-invariant distance measure.
    • To propose a corrected theoretical framework for blur-invariant image comparison.
    • To experimentally validate the performance of the original and corrected methods.

    Main Methods:

    • Theoretical analysis of the distance measure proposed by Gopalan et al. (2012).
    • Development of a revised mathematical formulation for blur-invariant image distance.

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  • Comparative experimental evaluation using image datasets with varying blur levels.

    • Main Results:

    • Two specific theoretical errors in the original distance measure were identified.
    • A corrected blur-invariant distance measure was derived and presented.
    • Experimental results demonstrate the superiority of the corrected method over the original.

    Conclusions:

    • The original blur-invariant distance measure by Gopalan et al. contains theoretical flaws.
    • The proposed correction significantly enhances the accuracy of blur-invariant image distance calculation.
    • The corrected method provides a more reliable tool for image analysis tasks sensitive to blur.