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Related Experiment Videos

A methodology for extracting objective color from images.

Mark W Powell1, Sudeep Sarkar, Dmitry B Goldgof

  • 1Mobility Systems Concept Development Section, Jet Propulsion Laboratory, Pasadena, CA 91109, USA. mpowell@csee.usf.edu

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|October 27, 2004
PubMed
Summary

This study introduces a new color correction method for indoor images, accurately modeling scene properties and camera settings. It achieves objective color measurements for applications like medical imaging and photorealistic rendering.

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Area of Science:

  • Computer Vision
  • Image Processing
  • Computational Photography

Background:

  • Accurate color reproduction in indoor environments is challenging due to complex lighting and surface interactions.
  • Existing color correction methods often lack the ability to model scene geometry and illuminant properties comprehensively.
  • Objective color measurement is crucial for various applications, including scientific analysis and digital content creation.

Purpose of the Study:

  • To develop a novel methodology for robust color correction of indoor images.
  • To explicitly model scene geometry, surface reflectance, and illuminant characteristics.
  • To enable objective color measurements from digital images.

Main Methods:

  • Utilized registered 3-D range images to capture surface orientation and location for detailed scene modeling.

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  • Developed algorithms to automatically identify and exclude regions unsuitable for color correction (e.g., specularities, textured areas).
  • Integrated models for illuminant location, surface reflectance, and camera responsivity.

    • Main Results:

    • Achieved objective color measurements of imaged surfaces.
    • Demonstrated successful application to diverse tasks including photorealistic rerendering, skin lesion detection, and burn scar analysis.
    • Validated the method's robustness across different lighting conditions and with multiple range scanners.

    Conclusions:

    • The presented methodology offers a comprehensive and integrated approach to indoor color correction, previously unavailable in computer vision.
    • This system provides a foundation for more accurate and reliable color analysis in various practical applications.
    • The ability to model scene specifics and exclude problematic regions leads to enhanced image quality and data integrity.