The accuracy of depth judgments that are based on binocular disparity or structure from motion (motion parallax and object rotation) was studied in 3 experiments. In Experiment 1, depth judgments were recorded for computer simulations of cones specified by binocular disparity, motion parallax, or stereokinesis. In Experiment 2, judgments were recorded for real cones in a structured environment, with depth information from binocular disparity, motion parallax, or object rotation about the y-axis. In both of these experiments, judgments from binocular disparity information were quite accurate, but judgments on the basis of geometrically equivalent or more robust motion information reflected poor recovery of quantitative depth information. A 3rd experiment demonstrated stereoscopic depth constancy for distances of 1 to 3 m using real objects in a well-illuminated, structured viewing environment in which monocular depth cues (e.g., shading) were minimized. It has been pointed out that the geometric information supporting the perception of depth from binocular disparity is actually less determinate than that supporting the recovery of structure from object rotation or motion parallax

Neurons in the visual cortex of monkeys respond selectively to the disparity between the images in the two eyes. Recent recordings have shown that some of the disparity-selective neurons in the primary visual cortex and the posterior parietal cortex are modulated by the distance of fixation. A population of such gain-modulated, disparity-selective neurons forms a set of basis functions of horizontal disparity and distance of fixation that can be used as an intermediate representation for computing egocentric distance. This distributed representation is consistent with psychophysical studies of human depth perception; in contrast, neurons explicitly tuned to distance are not consistent with how we perceive distance. In a population model that includes noise in the firing rates of neurons, the perceived distance is

depth ambiguities. Without promoting the cues, their raw data (e.g., disparities and velocities) are in different units so that simple cue-combination strategies, such as averaging the depth estimates made using each cue, are impossible. When the missing parameters are the eye positions (vergence, gaze directions, and torsions), the promotion process is referred to as depth scaling. In particular, in central gaze, the raw sensory data for the cue (velocities, disparities, etc.) are scaled by (that is, multiplied by, or multiplied by the square of) an estimate of the fixation distance. To the extent that this scaling is done accurately, the result is depth constancy: perceived depth that is independent of changes in viewing conditions. In this hapter we will limit our discussion of cue promotion to the issue of scaling by the fixation distance. We review a number of ways in which depth scaling may be accomplished. Micha

We propose a computational scheme for uncalibrated reconstruction of scene structure up to a relief transformation from binocular disparities. This scheme, which we call regional disparity correction (RDC), is motivated both by computational considerations and by psychophysical observations regarding human stereoscopic depth perception. We describe an implementation of RDC, and demonstrate its performance experimentally. As an example of applications of RDC, we show how it can be used to align a three-dimensional object model with an uncalibrated disparity field. Keywords: stereo, disparity, uncalibrated reconstruction, relief transformation, human vision, model alignment 1 Introduction Binocular disparities, i.e., the slightly different views captured by two eyes or cameras, can convey important information about the three-dimensional structure of the scene. In machine vision, the classical approach is to combine disparities with precise camera calibration information in order to r...

: : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : xiii I Introduction : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 1 A. Spatial representations and sensori-motor coordination : : : : : : : : : 1 B. The posterior parietal cortex : : : : : : : : : : : : : : : : : : : : : : : 2 C. Neural code for spatial representations : : : : : : : : : : : : : : : : : : 4 1. Dynamic remapping : : : : : : : : : : : : : : : : : : : : : : : : : : 4 2. Gain modulation : : : : : : : : : : : : : : : : : : : : : : : : : : : : 6 3. The Zipser and Andersen Network : : : : : : : : : : : : : : : : : : 6 D. Parallel vectorial representations : : : : : : : : : : : : : : : : : : : : : 9 E. Thesis Outline : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 10 1. Hierarchy in spatial representations : : : : : : : : : : : : : : : : : 10 2. A basis function approach for spatial representation : : : : : : : : 11 II Egocentric spatial representation in early vision : :...

The ratio of the vertical sizes of corresponding features in the two eyes' retinal images depends both on the associated object's distance and on its horizontal direction relative to the head (eccentricity). It is known that manipulations of vertical size ratio can affect perceived distance, size, depth and shape. We examined how observers use the vertical size ratio to determine the viewing distance. Do they use the horizontal gradient of vertical size ratio, or do they combine the vertical size ratio itself with the eccentricity at which it is found? Distance scaling (as measured by having subjects set an ellipsoid's size and shape to match a tennis ball) was no better when the judged object was 30 to the right of the head (where vertical size ratios vary considerably with distance) than when it was located straight ahead. Distance scaling improved when vertical disparities were presented within larger visual fields, irrespective of where this was relative to the head. Our results support the proposal that subjects use the horizontal gradient of vertical size ratio to estimate the distance of an object that they are looking at.

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