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Modeling Radiometric Uncertainty for Vision with Tone-Mapped Color Images.

Ayan Chakrabarti, Ying Xiong, Baochen Sun

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    |September 10, 2015
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    This summary is machine-generated.

    Tone-mapping in cameras distorts radiometric data. This study models the uncertainty in correcting this distortion, showing pixel reliability varies. The model aids computer vision analysis of tone-mapped images.

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    Area of Science:

    • Computer Vision
    • Image Processing
    • Radiometry

    Background:

    • Digital cameras use tone-mapping to display images, compressing dynamic range.
    • Tone-mapping introduces non-linear distortions, requiring radiometric calibration for computer vision analysis.
    • The uncertainty in undoing tone-mapping effects is not well understood.

    Purpose of the Study:

    • To model and quantify the uncertainty in undoing tone-mapping distortions.
    • To develop a method for fitting this uncertainty model to specific cameras or imaging pipelines.
    • To demonstrate the utility of uncertainty distributions in computer vision tasks.

    Main Methods:

    • Developing a mathematical model for tone-mapping uncertainty.
    • Creating a method to fit the uncertainty model to camera-specific data.
    • Incorporating pixel-wise uncertainty distributions into computer vision algorithms.

    Main Results:

    • The uncertainty of radiometric calibration varies significantly across the color space.
    • A model was developed that quantifies this uncertainty for each pixel.
    • The fitted model provides a probability distribution of linear scene colors for each pixel.

    Conclusions:

    • Pixel reliability in tone-mapped images is not uniform.
    • The proposed uncertainty model and fitting method enable more robust radiometric calibration.
    • Utilizing these uncertainty distributions improves estimation in computer vision tasks.