The aim of this study was to test red - green dichromats' ability to discriminate between illuminant and surface-reflectance changes in natural scenes. Stimuli were simulations of natural scenes presented on a colour monitor with 10-bit resolution per gun. The natural scenes were obtained with a fast hyperspectral imaging system. Six different scenes (including rocks, foliage, and buildings) were tested. In each trial, two images were presented in sequence, each for 1 s, with no interval. The images differed in the (...) phase of daylight on the scene: first with correlated colour temperature 25 000 K, then 6 700 K. The spectral reflectance of a region in the second image was changed randomly, consistent with a local change in daylight. The observer's task was to decide whether a particular surface in the successive images was the same (pure illuminant change) or different (illuminant change with a surface-reflectance change). The performance of four deuteranopes and five protanopes varied considerably across the scenes tested, with mean colour constancy indices (± 1 SEM) of 0.48 (± 0.13) and 0.19 (± 0.10), respectively (1.0 representing ideal performance). Deuteranopes seemed less disadvantaged than protanopes, performing close to normal with some scenes. (shrink)

The number of discriminable colours is often assumed to be of the order of several million but the extent of detectable chromatic diversity present in individual natural scenes is an open question. Here, the aim was to estimate the number of discriminable colours seen in natural scenes. Hyperspectral data were obtained from a set of natural scenes over the range 400 - 720 nm at 10 nm intervals (Nascimento et al, 2002 Journal of the Optical Society of America A 19 (...) 1484 - 1490) and the representation of each scene in CIELAB space computed for all pixels of the image. The number of discriminable colours was estimated by counting the number of unit volumes in the three-dimensional volume defined by the scene when represented in CIELAB space. We found that the number of discriminable colours estimated in a scene could vary by an order of magnitude from scene to scene, but rarely exceeded 105, indicating a limited chromatic diversity. (shrink)

Colour constancy refers to the ability to extract information about surface colours independently of illumination conditions. A ripe strawberry, for example, appears the same red when viewed under a blue sky or a reddish sunset. Since Land's pioneering work, discussion has centred on the issue whether colour constancy is achieved primarily in the retina or visual cortex. Recently, the debate has shifted to a consideration of the constraints imposed by various psychophysical tasks and instructions. Humans can judge illuminant colour, reflected-light (...) colour, surface colour, and the relationship between surface colours within a scene. Such observations suggest that colour constancy may not be a unitary phenomenon, and that its different aspects may be mediated at different levels in the visual system. Several questions therefore arise: are the tasks of surface estimation and illuminant estimation complementary, insofar as good performance in one implies poor performance in the other? How does retinal adaptation alter performance? Is colour vision tuned to the statistical composition of natural surfaces and illuminants? What role does colour-opponent processing in retina and cortex play in colour constancy? These and related questions are explored here in order to better understand what we mean when we ask: "Why do strawberries look red?". (shrink)

A series of experiments was performed in which subjects indicated whether two four-dot patterns were the same, although possibly viewed from different directions, or different, paired at random. Analyses of responses times and error rates suggest that the subjects' performance in this affine matching task is based on non-accidental properties such as convexity, parallelism, collinearity, and proximity, rather than on real affine invariants such as the ratio of triangular areas.