Previous investigators have shown that observers' visual cue combination strategies are remarkably flexible in the sense that these strategies adapt on the basis of the estimated reliabilities of the visual cues. However, these researchers have not addressed how observers' acquire these estimated reliabilities. This article studies observers' abilities to learn cue combination strategies. Subjects made depth judgments about simulated cylinders whose shapes were indicated by motion and texture cues. Because the two cues could indicate different shapes, it was possible to design tasks in which one cue provided useful information for making depth judgments, whereas the other cue was irrelevant. The results of experiment 1 suggest that observers' cue combination strategies are adaptable as a function of training; subjects adjusted their cue combination rules to use a cue more heavily when the cue was informative on a task versus when the cue was irrelevant. Experiment 2 demonstrated that experience-dependent adaptation of cue combination rules is context-sensitive. On trials with presentations of short cylinders, one cue was informative, whereas on trials with presentations of tall cylinders, the other cue was informative. The results suggest that observers can learn multiple cue combination rules, and can learn to apply each rule in the appropriate context. Experiment 3 demonstrated a possible limitation on the context-sensitivity of adaptation of cue combination rules. One cue was informative on trials with presentations of cylinders at a left oblique orientation, whereas the other cue was informative on trials with presentations of cylinders at a right oblique orientation. The results indicate that observers did not learn to use different cue combination rules in differe...

We introduce a learning algorithm for the weights in a very common class of discrimination functions usually called "weighted average". The learning algorithm can reduce the number of free variables by simple but effective a priori criteria about significant features. Here we apply our algorithm to three tasks of different dimensionality all concerned with face recognition. 1 Introduction Many pattern recognition systems can be roughly divided into two parts, feature extraction and pattern discrimination. In feature extraction an input I is transformed into a vector I k 2 IR N . (In speech recognition I k can, for example, represent the Fourier transformation in a certain time interval in a specific frequency band [14]; in image processing I k could be the filter response of a wavelet-like filter at a certain position in the grey-level picture [11, 15]). In discrimination the input I has to be assigned to a specific class c. The extracted features are used to evaluate certain simila...

We studied whether the blur/sharpness of an occlusion boundary between a sharply focused surface and a blurred surface is used as a relative depth cue. Observers judged relative depth in pairs of images that differed only in the blurriness of the common boundary between two adjoining texture regions, one blurred and one sharply focused. Two experiments were conducted; in both, observers consistently used the blur of the boundary as a cue to relative depth. However, the strength of the cue, relative to other cues, varied across observers. The occlusion edge blur cue can resolve the near/far ambiguity inherent in depth-from-focus computations. Introduction Faced with the task of inferring the 3D geometry of a scene from 2D images, the human observer uses a wide range of cues. In single 2D images, occlusion, perspective, shading, shadows, texture gradients, and familiar size can all provide information about 3D geometry; stereo comparison of two 2D images provides another cue. Most of th...

In this report we review a large body of literature describing how experience affects recognition. Both neurophysiology and psychophysics provide clear evidence for the development of recognition over time. In particular, we show how perceptual learning in recognition tasks can be directly linked to learning in feature tuned inferotemporal lobe neurons in the primate brain. The environment as we experience it, is so structured that potentially very different images appearing in close temporal succession are likely to be views of the same object. We argue that this temporal structure forms the basis of a tendency (a prior in the sense of Bayesian Statistics) of the human visual system to associate images of objects together over short periods of time.

An optimal linear system for integrating visual cues to 3D surface geometry weights cues in inverse proportion to their uncertainty. The problem of integrating texture and stereo information for judgments of planar surface slant provides a strong test of optimality in human perception. Since the accuracy of slant from texture judgments changes by an order of magnitude from low to high slants, optimality predicts corresponding changes in cue weights as a function of surface slant. Furthermore, since humans show significant individual differences in their abilities to use both texture and stereo information for judgments of 3D surface geometry, the problem admits the stronger test that individual differences in subjectsÕ thresholds for discriminating slant from the individual cues should predict individual differences in cue weights. We tested both predictions by measuring slant discrimination thresholds and stereo/texture cue weights as a function of surface slant for multiple subjects. The results bear out both predictions of optimality, with the exception of an apparent slight under-weighting of texture information. This may be accounted for by factors specific to the stimuli used to isolate stereo information in the experiments. Taken together, the results are consistent with the hypothesis that humans optimally combine the two cues to surface slant, with cue weights proportional to the subjective reliability of the cues.

The process by which the human visual system parses an image into contours, surfaces, and objects—perceptual grouping—has proven difficult to capture in a rigorous and general theory. A natural candidate for such a theory is Bayesian probability theory, which provides optimal interpretations of data under conditions of uncertainty. But the fitofBayesian theory to human grouping judgments has never been tested, in part because methods for expressing grouping hypotheses probabilistically have not been available. This paper presents such methods for the case of contour integration; that is, the aggregation of a sequence of visual items into a “virtual curve. ” Two experiments are reported in which human subjects were asked to group ambiguous configurations of dots (in Exp. 1, a sequence of five dots could be judged to contain a “corner” or not; in Exp. 2, an arrangement of six dots could be judged to fall into two disjoint contours or one smooth contour). The Bayesian theory accounts extremely well for subjects ’ judgments, explaining more than 75 % of the variance in both tasks. The theory thus provides a far more quantitatively precise account of human contour integration than previously possible, allowing a very precise calculation of the subjective goodness of a virtual chain of dots. Because Bayesian theory is inferentially optimal, this finding suggests a “rational justification”—and hence possibly an evolutionary rationale—for some of the rules of perceptual grouping. Perceptual grouping is the process whereby individual items in the visual image are aggregated into larger structures. Grouping is known to influence many low-level visual computations, such as the perception of lightness (Adelson,

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We test hypotheses concerning human cue combination in a slant estimation task. Observers

ers' performance when compared to that of a nonmoving observer receiving similar optic information. Other studies have found that selfmotion helps to resolve discrete symmetries in optic flow [8, 9], or to decrease integration times in SfM [10]. In the first experiment we tested extraretinal contributions to the extraction of depth from motion by means of a cue-conflict paradigm, in which motion parallax cues to 3D structure were weighed against conflicting linear perspective (i.e., the assumption that lines nearly parallel or perpendicular in the image are actually parallel or perpendicular in 3D space). The observer saw a planar 3D grid in motion, and provided an estimate of its `tilt' (i.e., the direction of its normal relative to the frontoparallel plane [11]). Motion parallax could be actively produced or passively observed. In the active case, parallax was due to the observer's head movements around a virtual object; in the passive case, the observer remained still while watchin

In this paper, we investigate how symmetry can be used to perceive 3D surface orientation. When a symmetric planar object is viewed from an angle, the projected contour has skew symmetry, which provides partial information about the 3D orientation of the object. For a given skew symmetry, this information can be characterized by a constraint curve of possible slant/tilt combinations that are consistent with a mirror-symmetric interpretation. These constraint curves move around when an object is rotated within a plane, and depend on what we will term the spin of the object: the angle between its axis of symmetry and the direction of tilt. To test the influence of symmetry constraint curves, we presented subjects with stereo images of symmetric objects that varied in spin, and had them perform an orientation-matching task. We found that the judgments showed biases that depended on the spin of the objects. Since other sources of information depend only on slant and tilt, not on spin, the biases imply that skew symmetry contributed to subjects’ judgments. In a second experiment, we introduced conflicts between stereo and symmetry cues, and found that the spin-dependent biases can be modulated by selectively changing stereo slant. We propose an explanation of these results involving the optimal integration of stereo and skew symmetry, and present a Bayesian model that