Understanding interactions between object colour, form, and light in human vision
Professor Anya Hurlbert, Newcastle University, UK
Without light, there would be no colour. But, in human vision, colour is not simply determined by light. Rather, colour is the result of complex interactions between light, surfaces, eyes and brains. Embedded in these are the neural mechanisms of colour constancy, which stabilise object colours under changes in the illumination spectrum. Yet, colour constancy is not perfect, and predicting the colour appearance of a particular object for a particular individual, when viewed under a particular illumination, is not simple. Yellow bananas may remain ever yellow even under fluorescent lamps, but blue dresses may turn white under ambiguous lights. Various factors affect colour appearance: the shape of the object, whether it is 2D or 3D, of a recognisable form or not; its other surface properties, whether it is glossy or matte, textured or uniform; and individual variations in visual processing. The shape of the illumination spectrum also affects colour appearance: metamerism in contemporary lighting provides a new challenge to colour constancy. How to measure colour – a subjective experience – most reliably is another challenge for vision scientists. This talk will describe psychophysical experiments and theory addressing these considerations in our understanding of object colour perception by humans.
Critical contours link generic image flows to salient surface organisation
Professor Steven Zucker, Yale University, USA
Shape inferences from images, or line drawings, are classical ill-posed inverse problems. Computational researchers mainly seek 'priors' for regularisation, e.g. regarding the light source, or scene restrictions for training neural networks, such as indoor rooms. While of technical interest, such solutions differ in two fundamental ways from human perception: (i) our inferences are largely robust across lighting and scene variations; and (ii) individuals only perceive qualitatively, not quantitatively, the same shape from a given image. Importantly, we know from psychophysics that similarities across individuals concentrate near certain configurations, such as ridges and boundaries, and it is these configurations that are often represented in line drawings. Professor Zucker will introduce a method for inferring qualitative 3D shape from shading that is consistent with these observations. For a given shape, certain shading patches become equivalent to “line drawings” in a well-defined shading-to-contour limit. Under this limit, and invariantly, the contours partition the surface into meaningful parts using the Morse-Smale complex. Critical contours are the (perceptually) stable parts of this complex and are invariant over a wide class of rendering models. The result provides a topological organisation of the surface into 'bumps' and 'dents' from the underlying shading geometry, and provides an invariant linking image gradient flows to surface organisation.
Colour and illumination in computer vision
Professor Graham Finlayson, University of East Anglia, UK
In computer vision, illumination is considered to be a problem that needs to be ‘solved’. The colour bias due to illumination is removed to support colour-based image recognition, stable tracking (in and out of shadows) amongst other tasks. In this talk Professor Finlayson will review historical and current algorithms for illumination estimation. In the classical approach, the illuminant colour is estimated by an - ever more sophisticated - analysis of simple image summary statistics. More recently, the full power - and much higher complexity - of deep learning has been deployed (where, effectively, the definition of the image statistics of interest are found as part of the overall optimisation). Professor Finlayson will challenge the orthodoxy of deep learning i.e. that it is the obvious solution to illuminant estimation. Instead he will propose that the estimates made by simple algorithms are biased and this bias can be corrected to deliver leading performance. The key new observation in our method - bias correction has been tried before with limited success - is that the bias must be corrected in an exposure invariant way.
Resolution of visual ambiguity: interactions in the perception of colour, material and illumination
Professor David H Brainard, University of Pennsylvania, USA
A key goal of biological image understanding is to extract useful perceptual representations of the external world from the images that are formed on the retina. This is challenging for a variety of reasons, one of which is that multiple configurations of the external world can produce the same retinal image, making the image-understanding problem fundamentally under constrained. None-the-less, biological systems are able to combine constraints provided by the retinal image with those provided by statistical regularities in the natural environment to produce representations that are well correlated with physical properties of the external world. One example of this is provided by our ability to perceive object colour and material properties. These percepts are correlated with object spectral surface and geometric surface reflectance, respectively. But the retinal image formed of an object depends not only on object surface reflectance, but also on the spatial and geometric properties of the illumination. This dependence in turn leading to ambiguity that perceptual processing must resolve. To understand how such processing works, it is necessary to measure how perception is used to identify object colour and material, and how such identification depends on object surface reflectance (both spatial and spectral) as well as on object-extrinsic factors such as the illumination. Classically, such measurements have been made using indirect techniques, such as matching by adjustment or naming. This talk will introduce a novel measurement method that uses object selection directly, together with a model of underlying perceptual representation, to study the stability of object colour across changes in illumination, as well as how object colour and material trade off in identification.