The importance of spatial visual scene parameters in predicting optimal cone sensitivities in routinely trichromatic frugivorous old-world primates

Computational models that predict the spectral sensitivities of primate cone photoreceptors have focussed only on the spectral, not spatial, dimensions. On the ecologically valid task of foraging for fruit, such models predict the M-cone (“green”) peak spectral sensitivity 10–20 nm further from the L-cone (“red”) sensitivity peak than it is in nature and assume their separation is limited by other visual constraints, such as the requirement of high-acuity spatial vision for closer M and L peak sensitivities. We explore the possibility that a spatio-chromatic analysis can better predict cone spectral tuning without appealing to other visual constraints. We build a computational model of the primate retina and simulate chromatic gratings of varying spatial frequencies using measured spectra. We then implement the case study of foveal processing in routinely trichromatic primates for the task of discriminating fruit and leaf spectra. We perform an exhaustive search for the configurations of M and L cone spectral sensitivities that optimally distinguish the colour patterns within these spectral images. Under such conditions, the model suggests that: (1) a long-wavelength limit is required to constrain the L cone spectral sensitivity to its natural position; (2) the optimal M cone peak spectral sensitivity occurs at ~525 nm, close to the observed position in nature (~535 nm); (3) spatial frequency has a small effect upon the spectral tuning of the cones; (4) a selective pressure toward less correlated M and L spectral sensitivities is provided by the need to reduce noise caused by the luminance variation that occurs in natural scenes

Luminance cues constrain chromatic blur discrimination in natural scene stimuli

Introducing blur into the color components of a natural scene has very little effect on its percept, whereas blur introduced into the luminance component is very noticeable. Here we quantify the dominance of luminance information in blur detection and examine a number of potential causes. We show that the interaction between chromatic and luminance information is not explained by reduced acuity or spatial resolution limitations for chromatic cues, the effective contrast of the luminance cue, or chromatic and achromatic statistical regularities in the images. Regardless of the quality of chromatic information, the visual system gives primacy to luminance signals when determining edge location. In natural viewing, luminance information appears to be specialized for detecting object boundaries while chromatic information may be used to determine surface properties

A low-cost hyperspectral scanner for natural imaging and the study of animal colour vision above and under water

Hyperspectral imaging is a widely used technology for industrial and scientific purposes, but the high cost and large size of commercial setups have made them impractical for most basic research. Here, we designed and implemented a fully open source and low-cost hyperspectral scanner based on a commercial spectrometer coupled to custom optical, mechanical and electronic components. We demonstrate our scanner's utility for natural imaging in both terrestrial and underwater environments. Our design provides sub-nm spectral resolution between 350-950 nm, including the UV part of the light spectrum which has been mostly absent from commercial solutions and previous natural imaging studies. By comparing the full light spectra from natural scenes to the spectral sensitivity of animals, we show how our system can be used to identify subtle variations in chromatic details detectable by different species. In addition, we have created an open access database for hyperspectral datasets collected from natural scenes in the UK and India. Together with comprehensive online build- and use-instructions, our setup provides an inexpensive and customisable solution to gather and share hyperspectral imaging data

Psychophysical Determination of the Relevant Colours That Describe the Colour Palette of Paintings

In an early study, the so-called “relevant colour” in a painting was heuristically introduced as a term to describe the number of colours that would stand out for an observer when just glancing at a painting. The purpose of this study is to analyse how observers determine the relevant colours by describing observers’ subjective impressions of the most representative colours in paintings and to provide a psychophysical backing for a related computational model we proposed in a previous work. This subjective impression is elicited by an efficient and optimal processing of the most representative colour instances in painting images. Our results suggest an average number of 21 subjective colours. This number is in close agreement with the computational number of relevant colours previously obtained and allows a reliable segmentation of colour images using a small number of colours without introducing any colour categorization. In addition, our results are in good agreement with the directions of colour preferences derived from an independent component analysis. We show that independent component analysis of the painting images yields directions of colour preference aligned with the relevant colours of these images. Following on from this analysis, the results suggest that hue colour components are efficiently distributed throughout a discrete number of directions and could be relevant instances to a priori describe the most representative colours that make up the colour palette of paintings.FEDER Funds by the Spanish Ministry of Science Innovation and Universities (MICINN, grant number RTI2018-094738-B-I00