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Related Experiment Video

Updated: May 8, 2026

Simultaneous Evaluation of Cerebral Hemodynamics and Light Scattering Properties of the In Vivo Rat Brain Using Multispectral Diffuse Reflectance Imaging
07:06

Simultaneous Evaluation of Cerebral Hemodynamics and Light Scattering Properties of the In Vivo Rat Brain Using Multispectral Diffuse Reflectance Imaging

Published on: May 7, 2017

Evaluating combinational illumination estimation methods on real-world images.

Bing Li, Weihua Xiong, Weiming Hu

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |August 27, 2013
    PubMed
    Summary
    This summary is machine-generated.

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    Combining illumination estimates from multiple methods improves accuracy for color constancy and white balancing. Trained methods, especially support vector regression, outperform others, even in complex multi-illuminant scenes.

    Area of Science:

    • Computer Vision
    • Image Processing
    • Computational Photography

    Background:

    • Illumination estimation is crucial for color constancy and automatic white balancing.
    • Combining estimates from multiple methods aims to enhance accuracy by fusing information.
    • Existing combinational methods vary in their reliance on supervised training and scene content cues.

    Purpose of the Study:

    • Evaluate the effectiveness of fusing multiple illumination estimates.
    • Identify the optimal combination method.
    • Analyze factors influencing combinational method performance.
    • Assess combination effectiveness in multi-illuminant scenes.

    Main Methods:

    • Survey and categorize existing combinational illumination estimation methods.

    Related Experiment Videos

    Last Updated: May 8, 2026

    Simultaneous Evaluation of Cerebral Hemodynamics and Light Scattering Properties of the In Vivo Rat Brain Using Multispectral Diffuse Reflectance Imaging
    07:06

    Simultaneous Evaluation of Cerebral Hemodynamics and Light Scattering Properties of the In Vivo Rat Brain Using Multispectral Diffuse Reflectance Imaging

    Published on: May 7, 2017

  • Categorization based on supervised training and use of scene content cues.
  • Conduct extensive tests on three real-world image datasets with labeled high-level features.

    • Main Results:

    • Trained combinational methods significantly outperform non-combinational methods.
    • Direct combination using support vector regression shows superior performance.
    • Trained methods are more effective than those relying on scene content cues.
    • Combination methods are effective even in challenging multiple-illuminant scenes.

    Conclusions:

    • Fusing illumination estimates from multiple subordinate methods enhances performance.
    • Trained combinational approaches, particularly support vector regression, are highly effective.
    • Scene content cues are less reliable than trained methods for illumination estimation.