The prior statistics of object colors

Koenderink, Jan J.

doi:10.1364/josaa.27.000206

Cited by 16 publications

(35 citation statements)

References 39 publications

(51 reference statements)

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“…Therefore, the database does not seem to be specifically favoring the error minimization strategy. Nevertheless, given the practical impossibility of achieving a truly unbiased database [32], the definitive way to confirm these suggestions on the optimality strategy is extending the Webster and Mollon's measures [19] performing both color discrimination and corresponding-pairs experiments in which observers are adapted to the same (controlled) statistics as the ones used in the numerical simulations. Recent experiments in color discrimination seem to follow this direction [83,84].…”

Section: Discussionmentioning

confidence: 98%

“…As in any finite color database, a certain bias is expected [32]. Figure 6 displays the existing bias in the collected database.…”

Section: Procedures For the Simulation Of Color Mechanisms Behavior Usmentioning

confidence: 98%

“…For example, in vision, it is known that the spectral reflectance of objects is intrinsically low dimensional [30]. Moreover, the physical constraints on the reflectance as well as the geometry of the surfaces give rise to particular statistics of the tristimulus values [31,32]. However, these distributions are also modified by additional, eventually noninteresting, causes [33]: both mutual illumination in surfaces of complex geometry and changes in the spectral illumination and its geometry introduce nonlinear changes in the PDF of the tristimulus values that are difficult to characterize.…”

Section: Non-uniformities and Shifts In Color Manifoldsmentioning

confidence: 99%

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Nonlinearities and Adaptation of Color Vision from Sequential Principal Curves Analysis

et al. 2012

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Mechanisms of human color vision are characterized by two phenomenological aspects: the system is nonlinear and adaptive to changing environments. Conventional attempts to derive these features from statistics use separate arguments for each aspect. The few statistical approaches that do consider both phenomena simultaneously follow parametric formulations based on empirical models. Therefore, it may be argued that the behavior does not come directly from the color statistics but from the convenient functional form adopted. In addition, many times the whole statistical analysis is based on simplified databases that disregard relevant physical effects in the input signal, as for instance by assuming flat Lambertian surfaces.In this work, we address the simultaneous statistical explanation of (i) the nonlinear behavior of achromatic and chromatic mechanisms in a fixed adaptation state, and (ii) the change of such behavior, i.e. adaptation, under the change of observation conditions. Both phenomena emerge directly from the samples through a single data-driven method: the Sequential Principal Curves Analysis (SPCA) with local metric. SPCA is a new manifold learning technique to derive a set of sensors adapted to the manifold using different optimality criteria. Moreover, in order to reproduce the empirical adaptation reported under D65 and A illuminations, a new database of colorimetrically calibrated images of natural objects under these illuminants was gathered, thus overcoming the limitations of available databases.The results obtained by applying SPCA show that the psychophysical behavior on color discrimination thresholds, discount of the illuminant and corresponding pairs in asymmetric color matching, emerge directly from realistic data regularities assuming no a priori functional form. These results provide stronger evidence for the hypothesis of a statistically driven organization of color sensors. Moreover, the obtained results suggest that color perception at this low abstraction level may be guided by an error minimization strategy rather than by the information maximization principle.

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Section: Discussionmentioning

confidence: 98%

“…As in any finite color database, a certain bias is expected [32]. Figure 6 displays the existing bias in the collected database.…”

Section: Procedures For the Simulation Of Color Mechanisms Behavior Usmentioning

confidence: 98%

Section: Non-uniformities and Shifts In Color Manifoldsmentioning

confidence: 99%

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Nonlinearities and Adaptation of Color Vision from Sequential Principal Curves Analysis

et al. 2012

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“…Second, it would be important to determine metamer mismatch volumes for metamers in the visual environment rather than the theoretical metamer mismatch volumes used here. Most real reflectances in the visual environment have particular properties (Koenderink, 2010), and hence some metamer mismatches within the metamer mismatch volume are much more probable to occur in the visual environment than others.…”

Section: Uncertainty Of Surface Colorsmentioning

confidence: 99%

Uncertainty of sensory signal explains variation of color constancy

et al. 2016

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Color constancy is the ability to recognize the color of an object (or more generally of a surface) under different illuminations. Without color constancy, surface color as a perceptual attribute would not be meaningful in the visual environment, where illumination changes all the time. Nevertheless, it is not obvious how color constancy is possible in the light of metamer mismatching. Surfaces that produce exactly the same sensory color signal under one illumination (metamerism) may produce utterly different sensory signals under another illumination (metamer mismatching). Here we show that this phenomenon explains to a large extent the variation of color constancy across different colors. For this purpose, color constancy was measured for different colors in an asymmetric matching task with photorealistic images. Color constancy performance was strongly correlated to the size of metamer mismatch volumes, which describe the uncertainty of the sensory signal due to metamer mismatching for a given color. The higher the uncertainty of the sensory signal, the lower the observers' color constancy. At the same time, sensory singularities, color categories, and cone ratios did not affect color constancy. The present findings do not only provide considerable insight into the determinants of color constancy, they also show that metamer mismatch volumes must be taken into account when investigating color as a perceptual property of objects and surfaces.

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“…For this reason, here we concentrate on the results with the cone sensitivities of Stockman and Sharpe (2000). Koenderink (2010) argued that the characteristics of the cone sensitivities may be at the source of invariances in the distribution of color signals. Figure 9a shows the sensitivity spectra of the cones according to Stockman and Sharpe (2000).…”

Section: Sensorsmentioning

confidence: 99%

What determines the relationship between color naming, unique hues, and sensory singularities: Illuminations, surfaces, or photoreceptors?

2015

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The relationship between the sensory signal of the photoreceptors on one hand and color appearance and language on the other hand is completely unclear. A recent finding established a surprisingly accurate correlation between focal colors, unique hues, and so-called singularities in the laws governing how sensory signals for different surfaces change across illuminations. This article examines how this correlation with singularities depends on reflectances, illuminants, and cone sensitivities. Results show that this correlation holds for a large range of illuminants and for a large range of sensors, including sensors that are fundamentally different from human photoreceptors. In contrast, the spectral characteristics of the reflectance spectra turned out to be the key factor that determines the correlation between focal colors, unique hues, and sensory singularities. These findings suggest that the origins of color appearance and color language may be found in particular characteristics of the reflectance spectra that correspond to focal colors and unique hues.

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The prior statistics of object colors

Cited by 16 publications

References 39 publications

Nonlinearities and Adaptation of Color Vision from Sequential Principal Curves Analysis

Nonlinearities and Adaptation of Color Vision from Sequential Principal Curves Analysis

Uncertainty of sensory signal explains variation of color constancy

What determines the relationship between color naming, unique hues, and sensory singularities: Illuminations, surfaces, or photoreceptors?

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