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Vector Correlation of Color Images

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Auto-correlation and cross-correlation have been used in signal and image processing for many years, but have only recently been applied to color images. Since the correlation of individual signals and gray-scale images yields a measure of structural similarity, in each of their dimensions, the correlation of n-dimensional signals should yield similarity measures of n-dimensional space. This paper presents, for the first time, evidence that a vector correlation peak encodes both the structural similarity and also a mapping of the vector-space rotation between two color images.
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Document Type: Research Article

Publication date: January 1, 2002

More about this publication?
  • Started in 2002 and merged with the Color and Imaging Conference (CIC) in 2014, CGIV covered a wide range of topics related to colour and visual information, including color science, computational color, color in computer graphics, color reproduction, volor vision/psychophysics, color image quality, color image processing, and multispectral color science. Drawing papers from researchers, scientists, and engineers worldwide, DGIV offered attendees a unique experience to share with colleagues in industry and academic, and on national and international standards committees. Held every year in Europe, DGIV papers were more academic in their focus and had high student participation rates.

    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 papers for details.

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