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Using Linear Models for the Illumination-Invariant Classification of Color Textures

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Spatial structure in a color image can be represented using correlation functions defined within and between sensor bands. Using a linear model for surface spectral reflectance with the same number of parameters as the number of classes of photoreceptors, we show that illumination changes correspond to linear transformations of a surface correlation matrix. From this relationship, we derive a distance function for comparing sets of spatial correlation functions that can be used for illumination-invariant recognition. We demonstrate using a large body of experiments that this distance function can be used for accurate texture classification in the presence of large changes in illumination spectral distribution.
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Document Type: Research Article

Publication date: January 1, 1995

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  • CIC is the premier annual technical gathering for scientists, technologists, and engineers working in the areas of color science and systems, and their application to color imaging. Participants represent disciplines ranging from psychophysics, optical physics, image processing, color science to graphic arts, systems engineering, and hardware and software development. While a broad mix of professional interests is the hallmark of these conferences, the focus is color. CICs traditionally offer two days of short courses followed by three days of technical sessions that include three keynotes, an evening lecture, a vibrant interactive (poster) papers session, and workshops. An endearing symbol of the meeting is the Cactus Award, given each year to the author(s) of the best interactive paper; there are also Best Paper and Best Student Paper awards.

    Please note: for Purposes of its Digital Library content, IS&T defines Open Access as papers that will be downloadable in their entirety for free in perpetuity. Copyright restrictions on papers vary; see individual paper for details.

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