When a person looks at an object while exploring it with their hand, vision and touch both provide information for estimating the properties of the object. Vision frequently dominates the integrated visual-haptic percept, for example when judging size, shape or position, but in some circumstances the percept is clearly affected by haptics. Here we propose that a general principle, which minimizes variance in the final estimate, determines the degree to which vision or haptics dominates. This principle is realized by using maximum-likelihood estimation to combine the inputs. To investigate cue combination quantitatively, we first measured the variances associated with visual and haptic estimation of height. We then used these measurements to construct a maximum-likelihood integrator. This model behaved very similarly to humans in a visual-haptic task. Thus, the nervous system seems to combine visual and haptic information in a fashion that is similar to a maximum-likelihood integrator. Visual dominance occurs when the variance associated with visual estimation is lower than that associated with haptic estimation.

Humans integrate visual and haptic information in a statistically optimal fashion.

Let's read! We will often find out this sentence everywhere. When still being a kid, mom used to order us to always read, so did the teacher. Some books are fully read in a week and we need the obligation to support reading. What about now? Do you still love reading? Is reading only for you who have obligation? Absolutely not! We here offer you a new book enPDFd the perception of the visual world to read.

The Perception of the Visual World. By James J. Gibson. U.S.A.: Houghton Mifflin Company, 1950 (George Allen & Unwin, Ltd., London). Price 35s.

https://win.pisavisionlab.org/teaching/burr/Ernst_Bulthoff.pdf

Merging the senses into a robust percept

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.

/pdf/object-perception-as-bayesian-inference-2ors0mo4ec.pdf

Object Perception as Bayesian Inference

Publisher Summary
This chapter discusses three questions: Why are there so many sources of information about layout? How is it that one perceives layout with near-metric accuracy when none of these sources yields metric information about it? Can one not do better, theoretically, in understanding the perception of layout than simply make a list? The answer to the first question begins with Todd's answer. Perceiving layout is extremely important to human beings, so important that it must be redundantly specified so that the redundancy can guard against the failure of any given source or the failure of any of the assumptions on which a given source is based. However, information redundancy is only part of the answer. Different sources of information about layout metrically reinforce and contrast with each other, providing a powerful network of constraints. The answer to the second proceeds from this idea. Through the analysis of depth-threshold functions for nine different sources of information about layout, one can begin to understand how those sources of information sharing the same-shaped functions across distances can help ramify judgments of layout by serving to correct measurement errors in each. Third, on the basis of the analyses and the pattern of functions, it suggests that list making has misled about space and layout. Psychologists and other vision scientists have generally considered layout, space, and distance as a uniform commodity in which observers carry out their day-to-day activities.

/pdf/perceiving-layout-and-knowing-distances-the-integration-1i3ri93gex.pdf

Perceiving Layout and Knowing Distances: The Integration, Relative Potency, and Contextual Use of Different Information about Depth*

Measurement and Modeling of Depth Cue Combination: in Defense of Weak Fusion

https://www.cns.nyu.edu/~msl/papers/youngetal93.pdf

A perturbation analysis of depth perception from combinations of texture and motion cues

We describe a series of experiments designed to test (1) whether human observers combine depth cues using a weighted average when depth estimates in different maps are nearly consistent, (2) whether human observers behave as robust estimators when depths become increasingly inconsistent, and (3) whether the weights used in the linear rule of combination change to reflect the estimated reliability of different depth cues. We report initial experiments concerning texture and motion. The data are clearly consistent with the notion that the depth percept is a linear combination of the individual depth values portrayed by each cue. By randomly varying the shapes of the texture elements, the texture cue is artificially made unreliable, and the data support the hypothesis that unreliable cues arc given less weight. Finally, there is an indication that when cues are strongly inconsistent, the weight on one of the cues is lowered, consistent with the hypothesis of robustness.

Mark J. Young

Papers

Measurement and Modeling of Depth Cue Combination: in Defense of Weak Fusion

A perturbation analysis of depth perception from combinations of texture and motion cues

Psychophysical estimation of the human depth combination rule

Depth cue anisotropies and weights