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

Our work on Active Vision has recently focused on the computational modelling of navigational tasks, where our investigations were guided by the idea of approaching vision for behavioral systems in form of modules that are directly related to perceptual tasks. These studies led us to branch in various directions and inquire into the problems that have to be addressed in order to obtain an overall understanding of perceptual systems. In this paper we present our views about the architecture of vision systems, about how totackle the design and analysis of perceptual systems, and promising future research directions. Our suggested approach for understanding behavioral vision to realize the relationship of perception and action builds on two earlier approaches, the Medusa philosophy [3] and the Synthetic approach [15]. The resulting framework calls for synthesizing an artificial vision system by studying vision competences of increasing complexity and at the same time pursuing the integration of the perceptual components with action and learning modules. We expect that Computer Vision research in the future will progress in tight collaboration with many other disciplines that are concerned with empirical approaches to vision, i.e. the understanding of biological vision. Throughout the paper we describe biological findings that motivate computational arguments which we believe will influence studies of Computer Vision in the near future.

Binocular disparity and motion parallax are powerful cues to the relative depth between objects. However to recover absolute depth, either additional scaling parameters are required to calibrate the information provided by each cue, or it can be recovered through the combination of information from both cues (Richards, W. (1985). Structure from stereo and motion. Journal of the Optical Society of America, 2, 343 -- 349). However, not all tasks necessarily require a full specification of the absolute depth structure of a scene and so psychophysical performance may vary depending on the amount of information available, and the degree to which absolute depth structure is required. The experiments reported here used three different tasks that varied in the type of geometric information required in order for them to be completed successfully. These included a depth nulling task, a depth-matching task, and an absolute depth judgement (shape) task. Real world stimuli were viewed (i) monocularly with head movements, (ii) binocularly and static, or (iii) binocularly with head movements. No effect of viewing condition was found whereas there was a large effect of task. Performance was accurate on the matching and nulling tasks and much less accurate on the shape task. The fact that the same perceptual distortions were not evident in all tasks suggests that the visual system can switch strategy according to the demands of the particular task. No evidence was found to suggest that the visual system could exploit the simultaneous presence of disparity and motion parallax. 2000 Elsevier Science Ltd. All rights reserved.

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...

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