Illusory motion from shadows.
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Computer Graphics↗
Biomedical subjects
D C Knill
Publications and source records attributed to D C Knill.
Categorical local-shape perception.
How well do observers perceive the local shape of an object from its shaded image? This problem was addressed by first deriving a potential representation of local solid shape. The descriptor of local shape, called shape characteristic, provides a viewpoint-independent continuum between hyperbolic (saddle-shaped) and elliptic (egg-shaped) points. The ability of human observers to make categorical judgments of local solid shape was then studied. This question was investigated by using a smooth 'croissant', a simple object made of two connected regions of elliptic and hyperbolic points. Observers decided whether the surface was locally elliptic or hyperbolic at various points on the object. The task was natural, and the observers could reliably partition the shaded image of the object into two regions, one elliptic and one hyperbolic. The ability of observers to perform this partition shows that they can, at least implicitly, localize the parabolic curves on a surface. This ability to locate the parabolic curve could in turn be exploited for other purposes, for instance to segment an object into its parts.
Humans↗
Object classification for human and ideal observers.
We describe a novel approach, based on ideal observer analysis, for measuring the ability of human observers to use image information for 3D object perception. We compute the statistical efficiency of subjects relative to an ideal observer for a 3D object classification task. After training to 11 different views of a randomly shaped thick wire object, subjects were asked which of a pair of noisy views of the object best matched the learned object. Efficiency relative to the actual information in the stimuli can be as high as 20%. Increases in object regularity (e.g. symmetry) lead to increases in the efficiency with which novel views of an object could be classified. Furthermore, such increases in regularity also lead to decreases in the effect of viewpoint on classification efficiency. Human statistical efficiencies relative to a 2D ideal observer exceeded 100%, thereby excluding all models which are sub-optimal relative to the 2D ideal.
Depth Perception↗
Perception of surface contours and surface shape: from computation to psychophysics.
Contours projected from surface markings provide information for the perception of surface shape. The nature of this information depends on how the shapes of surface marking are constrained relative to the shapes of the surfaces upon which they lie. A natural constraint is that of figural regularity relative to the shape of an underlying surface. Such a constraint would be expressed in terms of the geodesic curvature of a marking, with markings having zero geodesic curvature (geodesics of a surface) being the prototypic regular figures. I propose a number of forms for a geodesic constraint and present psychophysical evidence from a contour-labeling experiment that the human visual system implicitly incorporates a geodesic constraint in the processing of reflectance contours.
Computer Simulation↗
Apparent surface curvature affects lightness perception.
The human visual system has the remarkable capacity to perceive accurately the lightness, or relative reflectance, of surfaces, even though much of the variation in image luminance may be caused by other scene attributes, such as shape and illumination. Most physiological, and computational models of lightness perception invoke early sensory mechanisms that act independently of, or before, the estimation of other scene attributes. In contrast to the modularity of lightness perception assumed in these models are experiments that show that supposedly 'higher-order' percepts of planar surface attributes, such as orientation, depth and transparency, can influence perceived lightness. Here we show that perceived surface curvature can also affect perceived lightness. The results of the earlier experiments indicate that perceiving luminance edges as changes in surface attributes other than reflectance can influence lightness. These results suggest that the interpretation of smooth variations in luminance can also affect lightness percepts.
Computer Simulation↗
Estimating illuminant direction and degree of surface relief.
Many algorithms for deriving surface shape from shading require an estimate of the direction of illumination. This paper presents a new estimator for illuminant direction, which also generates an estimate of the degree of surface relief, that is measured by the variance of surface orientation (the partial derivatives of surface depth). Surfaces are considered to be samples of a stochastic process representing depth as a function of position in the image plane. We derive an estimator for illuminant tilt that is based only on some general assumptions about the process. The assumptions are that the process is wide-sense stationary, strictly isotropic; and mean-square differentiable and that the second partial derivatives of surface depth are locally independent of the first partial derivatives. We develop an estimator of illuminant slant and degree of surface relief in two stages. In the first, we develop a general format for an estimator based on the same assumptions that are used for the tilt estimator. The second stage is the actual implementation of the estimator and requires the specification of a functional form for the local probability distribution of surface orientations. This approach contrasts with previous ones, which begin their development with an assumption of a particular distribution for surfaces. The approach has the advantage that it separates the problems of surface modeling and light-source estimation, permiting one to easily implement specific estimators for different surface models. We implement the illuminant slant estimator for surfaces that have a Gaussian distribution of surface orientations and show simulation results. Degraded performance in the presence of self-shadowing is discussed.
Depth Perception↗
Human discrimination of fractal images.
In order to transmit information in images efficiently, the visual system should be tuned to the statistical structure of the ensemble of images that it sees. Several authors have suggested that the ensemble of natural images exhibits fractal behavior and, therefore, has a power spectrum that drops off proportionally to 1/f beta (2 less than beta less than 4). In this paper we investigate the question of which value of the exponent beta describes the power spectrum of the ensemble of images to which the visual system is optimally tuned. An experiment in which subjects were asked to discriminate randomly generated noise textures based on their spectral drop-off was used. Whereas the discrimination-threshold function of an ideal observer was flat for different spectral drop-offs, human observers showed a broad peak in sensitivity for 2.8 less than beta less than 3.6. The results are consistent with, but do not provide direct evidence for, the theory that the visual system is tuned to an ensemble of images with Markov statistics.
Computer Simulation↗