Extracting or separating intrinsic information and illumination from natural images is important for better solving robot vision tasks. In this chapter, we first derive grayscale intrinsic images based on the tricolor attenuation model and the tricolor linear model. To obtain color intrinsic images, we then propose a pixel-wise orthogonal decomposition model for outdoor image illumination processing. In our model, we set up a linear equation set for each pixel value vector based on physically based shadow invariants, deduce a pixel-wise orthogonal decomposition for its solutions, and then get an illumination-invariant vector for each pixel value vector on an image. Only requiring pixel-wise calculation, our physical illumination modeling for color illumination-invariant image can be further applied to real-time applications to resist illumination variation. Finally, we present a new illumination-based color space, the intrinsic information and lighting level (IL) space. Its first two channels represent 2D intrinsic information, and the third channel is for lighting levels. The IL color space has a one-to-one correspondence with the RGB color space. One valuable benefit of the IL color space is that illumination-related processing can be realized by directly operating on the lighting channel. As an example, based on the extracted lighting channel, we propose a new algorithm to estimate the intrinsic-lighting level of an image such that the shadow-free color image and relighting series are obtained.


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    Titel :

    Intrinsic-Image Deriving and Decomposition


    Weitere Titelangaben:

    Research on Intelligent Manufacturing


    Beteiligte:
    Tian, Jiandong (Autor:in)


    Erscheinungsdatum :

    02.12.2021


    Format / Umfang :

    34 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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