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Group polarization is the strengthening of an original group attitude following the discussion of views within a group (Teger & Pruitt, 1967). That is, if a group initially favors a viewpoint, after discussion the group consensus is likely a stronger endorsement of the viewpoint. Conversely, if the group was initially opposed to a viewpoint, group discussion would likely lead to stronger opposition.
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A neutral atom consists of a positively charged nucleus surrounded by a negatively charged electron cloud. When placed in an external electric field, the external electric force pulls the electrons and nucleus apart, opposite to the intrinsic attraction between the nucleus and the electrons. The opposing forces balance each other with a slight shift between the center of masses of the nucleus and the electron cloud, resulting in a polarized atom. On the other hand, a few molecules, like water,...
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Determination of the Excitation and Coupling Rates Between Light Emitters and Surface Plasmon Polaritons
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Polarization Guided Specular Reflection Separation.

Sijia Wen, Yinqiang Zheng, Feng Lu

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    |August 17, 2021
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    Summary
    This summary is machine-generated.

    This study introduces a polarization-guided model for robust specular reflection separation. The method effectively distinguishes surface reflections from intrinsic colors, even under challenging illumination conditions.

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    Area of Science:

    • Computer Vision
    • Image Processing
    • Optical Physics

    Background:

    • Specular reflection in images deviates from intrinsic object colors.
    • Existing methods struggle with similar surface and illumination colors (near-duplicate challenge).

    Purpose of the Study:

    • Develop a robust specular reflection separation method.
    • Address the near-duplicate challenge in specular reflection removal.
    • Improve consistency in object surface appearance for various applications.

    Main Methods:

    • Derived a polarization-guided model incorporating polarization information.
    • Developed an iterative optimization strategy for separation.
    • Generated a polarization chromaticity image to reveal image geometry and cluster pixels by diffuse color.
    • Utilized ADMM (Alternating Direction Method of Multipliers) strategy for unified optimization.

    Main Results:

    • The polarization chromaticity image accurately clusters pixels with similar diffuse colors.
    • Specular separation was improved by using diffuse color clusters as an implicit prior.
    • The method successfully separated specular reflections jointly using RGB and polarimetric information.
    • Demonstrated faithful separation in challenging scenarios with diverse illumination.

    Conclusions:

    • The polarization-guided model effectively separates specular reflections, overcoming limitations of previous methods.
    • Polarization information enhances specular reflection separation, particularly in complex lighting.
    • The proposed method offers a robust solution for applications requiring accurate object surface appearance.