A DNA test to sex most birds.

We perceive the shapes and material properties of objects quickly and reliably despite the complexity and objective ambiguities of natural images. Typical images are highly complex because they consist of many objects embedded in background clutter. Moreover, the image features of an object are extremely variable and ambiguous owing to the effects of projection, occlusion, background clutter, and illumination. The very success of everyday vision implies neural mechanisms, yet to be understood, that discount irrelevant information and organize ambiguous or noisy local image features into objects and surfaces. Recent work in Bayesian theories of visual perception has shown how complexity may be managed and ambiguity resolved through the task-dependent, probabilistic integration of prior object knowledge with image features.

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Object Perception as Bayesian Inference

/pdf/the-black-swan-the-impact-of-the-highly-improbable-578rzpihbf.pdf

The Black Swan: The Impact of the Highly Improbable

Computational color constancy is a fundamental prerequisite for many computer vision applications. This paper presents a survey of many recent developments and state-of-the-art methods. Several criteria are proposed that are used to assess the approaches. A taxonomy of existing algorithms is proposed and methods are separated in three groups: static methods, gamut-based methods, and learning-based methods. Further, the experimental setup is discussed including an overview of publicly available datasets. Finally, various freely available methods, of which some are considered to be state of the art, are evaluated on two datasets.

Computational Color Constancy: Survey and Experiments

A human observer is able to recognize the color of objects irrespective of the light used to illuminate them. This is called color constancy. A digital camera uses a sensor to measure the reflected light, meaning that the measured color at each pixel varies according to the color of the illuminant. Therefore, the resulting colors may not be the same as the colors that were perceived by the observer. Obtaining color constant descriptors from image pixels is not only important for digital photography, but also valuable for computer vision, color-based automatic object recognition, and color image processing in general. This book provides a comprehensive introduction to the field of color constancy, describing all the major color constancy algorithms, as well as presenting cutting edge research in the area of color image processing. Beginning with an in-depth look at the human visual system, Ebner goes on to: examine the theory of color image formation, color reproduction and different color spaces; discuss algorithms for color constancy under both uniform and non-uniform illuminants; describe methods for shadow removal and shadow attenuation in digital images; evaluate the various algorithms for object recognition and color constancy and compare this to data obtained from experimental psychology; set out the different algorithms as pseudo code in an appendix at the end of the book. Color Constancy is an ideal reference for practising engineers, computer scientists and researchers working in the area of digital color image processing. It may also be useful for biologists or scientists in general who are interested in computational theories of the visual brain and bio-inspired engineering systems.

Color constancy

Objects in the natural world possess different visual attributes, including shape, colour, surface texture and motion. Previous perceptual studies have assumed that the brain analyses the colour of a surface independently of its three-dimensional shape and viewing geometry, although there are neural connections between colour and two-dimensional form processing early in the visual pathway. Here we show that colour perception is strongly influenced by three-dimensional shape perception in a novel, chromatic version of the Mach Card--a concave folded card with one side made of magenta paper and the other of white paper. The light reflected from the magenta paper casts a pinkish glow on the white side. The perceived colour of the white side changes from pale pink to deep magenta when the perceived shape of the card flips from concave to convex. The effect demonstrates that the human visual system incorporates knowledge of mutual illumination-the physics of light reflection between surfaces--at an early stage in colour perception.

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Perception of three-dimensional shape influences colour perception through mutual illumination

We explore the relative utility of shape from shading and binocular disparity for depth perception. Ray-traced images either featured a smooth surface illuminated from above (shading-only) or were defined by small dots (disparity-only). Observers judged which of a pair of smoothly curved convex objects had most depth. The shading cue was around half as reliable as the rich disparity information for depth discrimination. Shading- and disparity-defined cues where combined by placing dots in the stimulus image, superimposed upon the shaded surface, resulting in veridical shading and binocular disparity. Independently varying the depth delivered by each channel allowed creation of conflicting disparity-defined and shading-defined depth. We manipulated the reliability of the disparity information by adding disparity noise. As noise levels in the disparity channel were increased, perceived depths and variances shifted toward those of the now more reliable shading cue. Several different models of cue combination were applied to the data. Perceived depths and variances were well predicted by a classic maximum likelihood estimator (MLE) model of cue integration, for all but one observer. We discuss the extent to which MLE is the most parsimonious model to account for observer performance.

Optimal integration of shading and binocular disparity for depth perception.

https://www.cell.com/current-biology/pdf/S0960-9822(15)00494-7.pdf

The many colours of ‘the dress’

In recent years many tone mapping operators (TMOs) have been presented in order to display high dynamic range images (HDRI) on typical display devices. TMOs compress the luminance range while trying to maintain contrast. The inverse of tone mapping, inverse tone mapping, expands a low dynamic range image (LDRI) into an HDRI. HDRIs contain a broader range of physical values that can be perceived by the human visual system. We propose a new framework that approximates a solution to this problem. Our framework uses importance sampling of light sources to find the areas considered to be of high luminance and subsequently applies density estimation to generate an expand map in order to extend the range in the high luminance areas using an inverse tone mapping operator. The majority of today’s media is stored in the low dynamic range. Inverse tone mapping operators (iTMOs) could thus potentially revive all of this content for use in high dynamic range display and image based lighting (IBL). Moreover, we show another application that benefits quick capture of HDRIs for use in IBL.

A framework for inverse tone mapping

When a planar object is rotated with respect to a directional light source, the reflected luminance changes. If surface lightness is to be a reliable guide to surface identity, observers must compensate for such changes. To the extent they do, observers are said to be lightness constant. We report data from a lightness matching task that assesses lightness constancy with respect to changes in object slant. On each trial, observers viewed an achromatic standard object and indicated the best match from a palette of 36 grayscale samples. The standard object and the palette were visible simultaneously within an experimental chamber. The chamber illumination was provided from above by a theater stage lamp. The standard objects were uniformly-painted flat cards. Different groups of naive observers made matches under two sets of instructions. In the Neutral Instructions, observers were asked to match the appearance of the standard and palette sample. In the Paint Instructions, observers were asked to choose the palette sample that was painted the same as the standard. Several broad conclusions may be drawn from the results. First, data for most observers were neither luminance matches nor lightness constant matches. Second, there were large and reliable individual differences. To characterize these, a constancy index was obtained for each observer by comparing how well the data were accounted for by both luminance matching and lightness constancy. The index could take on values between 0 (luminance matching) and 1 (lightness constancy). Individual observer indices ranged between 0.17 and 0.63 with mean 0.40 and median 0.40. An auxiliary slant-matching experiment rules out variation in perceived slant as the source of the individual variability. Third, the effect of instructions was small compared to the inter-observer variability. Implications of the data for models of lightness perception are discussed.

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Marina Bloj

Papers

Perception of three-dimensional shape influences colour perception through mutual illumination

Optimal integration of shading and binocular disparity for depth perception.

The many colours of ‘the dress’

A framework for inverse tone mapping

Measurements of the effect of surface slant on perceived lightness.