Computing statistical properties of hue distributions for color image analysis

Daniel Crevier

doi:10.1117/12.150180

20 August 1993 Computing statistical properties of hue distributions for color image analysis

Daniel Crevier

Proceedings Volume 2055, Intelligent Robots and Computer Vision XII: Algorithms and Techniques; (1993) https://doi.org/10.1117/12.150180
Event: Optical Tools for Manufacturing and Advanced Automation, 1993, Boston, MA, United States

Abstract

Color images can be analyzed using two kinds of coordinate systems: rectangular systems based on primary colors (RGB), and cylindrical systems based on hue, saturation, and intensity (HSI). HSI systems match our intuitive understanding of colors and make it possible to name colors in knowledge bases, a significant advantage given the mushrooming use of declarative knowledge for image analysis. On the other hand, HSI systems give rise to singularities which result in undesirable instabilities, notably with respect to the statistical properties of hue distributions. Computing the mean and variance of a split distribution in the conventional manner would yield an unrealistically large variance and a mean hue in the blue-green region. The paper presents alternative ways of computing means and variances that avoid these effects. At the cost of a relatively slight numerical overhead, these computations generate results in agreement with our intuitive understanding of colors in split peak situations, and reduce to the standard definitions in well-behaved histograms. Recursive formulas are given for the calculation of these statistics, and an efficient algorithm is presented. Equivalence conditions between the results of the introduced procedures and conventional calculations are stated. Examples are given using actual color images.

Citation Download Citation

Daniel Crevier "Computing statistical properties of hue distributions for color image analysis", Proc. SPIE 2055, Intelligent Robots and Computer Vision XII: Algorithms and Techniques, (20 August 1993); https://doi.org/10.1117/12.150180

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