Nonlinearities and Adaptation of Color Vision from Sequential Principal Curves Analysis

Abstract

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.

Spatio-Chromatic Adaptation via Higher-Order Canonical Correlation Analysis of Natural Images

“…In the color psychophysics literature, physically different stimuli are referred to as corresponding if they give rise to the same perceived color when viewed under different conditions [28]–[30]. Corresponding colors illustrate the (purely) chromatic adaptation ability of the human visual system and form a standard benchmark for chromatic adaptation models, see, for example, [21].…”

“…In this (standard) visualization, the correspondence between the colors is not made explicit. Qualitative comparisons of different chromatic adaptation models are based on the arrangement of the points in the diagram [21].…”

“…We call it higher-order canonical correlation analysis (HOCCA). HOCCA is applied to a recently established database of natural images which were captured under two different lighting conditions, namely illumination CIE A, yellowish light, and illumination CIE D65, daylight [21]. Figure 2 depicts example images from the database.…”

Graph Matching for Adaptation in Remote Sensing

“…In the case of using local PCA or local ICA models [40], [41], the problem is that the local models are not linked in any way so there is no guarantee for a proper identification of the correspondence under strong deformations. Related methods that integrate the set of local representations into a single global non-linear representation [42], [43] solve this identification problem and have been used in adaptation [44]. However, they are hard to use in manifolds including bifurcations.…”

Lossless coding of hyperspectral images with principal polynomial analysis

“…PPA is a deflationary algorithm based on drawing a sequence of Principal Curves that address one dimension at a time [10]. These Principal Curves are analytic and each step in the sequence consists of two basic operations.…”