Abstract
In the framework of a simple analytical model, we quantitatively validate the statement that the “color world” is amenable to much more accurate and faster segmentation than the “gray world”. That results in significant facilitating conditions required for originating indispensable pop-out effect, and, probably, forms the basis of various cognitive phenomena connected with the color vision. Besides, we show that the known (from optics) Rayleigh criterion for separability of two gray objects is considerably softened for objects of different colors.
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Notes
- 1.
In the above relation, \(\varDelta n\) is some finite length along the normal to the boundary, which is small when compared to the object size.
- 2.
In computer image recognition, that process is termed as pre-processing and is used to reduce a noise by means of its averaging with some filter.
- 3.
We restrict ourselves to the case of two-color images, to which the plain (two-dimensional) space of color tones corresponds. Such a space could be described by the combination of two complex numbers. Within that space, any color, represented by the sum of yellow and red colors of varied intensities, is displayed as the point on the color plane with zero contribution of the blue color. Considering three-color (and, hence, three-dimensional) space would result in the significant complicating the model.
- 4.
Simpler forms of Eq. (4) comparing to original relations (2) is the result of the above-made “involuntary” choice of color phases leading to \(\varphi _y\approx \pi /2\). One could verify that all conclusions about the contrast of color images remain valid with some another choice of the dependency \(\varphi (\lambda )\). This is due to the fact that actual are not numerical values of colors phases, but gradient of the phase along the direction of its steepest variation which is the relative characteristics, analogical to the image contrast (to the map of phase variation, in the case considered).
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Meilikov, E., Farzetdinova, R. (2018). Color or Luminance Contrast – What Is More Important for Vision?. In: Kryzhanovsky, B., Dunin-Barkowski, W., Redko, V. (eds) Advances in Neural Computation, Machine Learning, and Cognitive Research. NEUROINFORMATICS 2017. Studies in Computational Intelligence, vol 736. Springer, Cham. https://doi.org/10.1007/978-3-319-66604-4_22
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