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Quantization noise as a determinant for color thresholds in machine vision Full article

Journal Journal of the Optical Society of America A: Optics and Image Science, and Vision
ISSN: 1084-7529
Output data Year: 2018, Volume: 35, Number: 4, Pages: B214 Pages count : DOI: 10.1364/josaa.35.00b214
Authors Palchikova Irina G. 1,2 , Smirnov E.S. 1 , Palchikov E.I. 2,3
Affiliations
1 Technological Design Institute of Scientific Instrument Engineering, Siberian Branch, Russian Academy of Sciences
2 Novosibirsk State University, Department of Physics
3 Lavrent’ev Institute of Hydrodynamics, Siberian Branch, Russian Academy of Sciences,

Abstract: Color discrimination simulation is applied to study a uniformity of the color space of machine vision devices whose operation is based on a three-component color model and which involve analog-to-digital conversion of signals with a resolution of 8 bits per channel. Algorithms for finding the intervals of the dominant wavelength and color saturation of a specimen are developed. The spectral dependence of intervals of color parameters calculated using the digital images is found. It is shown that machine vision possesses the color discrimination thresholds, which can be drawn in the CIE1931 xy chromaticity diagram in the form of equal-contrast ellipses similar to the MacAdam ellipses. At resolution of 6 bits, the size of a reference MacAdam’s ellipse is a little less than that of the machine vision ellipse’s sizes, and at resolution of 7 bits, it is a little more than that of the machine vision ellipse’s sizes. A hypothesis is proposed that implies that the process of an encoding of the visual neural signals may include procedures similar to an analog-to-digital conversion.
Cite: Palchikova I.G. , Smirnov E.S. , Palchikov E.I.
Quantization noise as a determinant for color thresholds in machine vision
Journal of the Optical Society of America A: Optics and Image Science, and Vision. 2018. V.35. N4. P.B214. DOI: 10.1364/josaa.35.00b214 WOS Scopus РИНЦ OpenAlex
Dates:
Submitted: Oct 2, 2017
Accepted: Jan 28, 2018
Published print: Mar 6, 2018
Identifiers:
Web of science: WOS:000428931500026
Scopus: 2-s2.0-85044578628
Elibrary: 35486415
OpenAlex: W2789798356
Citing:
DB Citing
OpenAlex 17
Elibrary 14
Scopus 13
Web of science 5
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