Ideal cue combination for localizing texture-defined edges

Abstract

Many visual tasks can be carried out by using several sources of information. The most accurate estimates of scene properties require the observer to utilize all available information and to combine the information sources in an optimal manner. Two experiments are described that required the observers to judge the relative locations of two texture-defined edges (a vernier task). The edges were signaled by a change across the edge of two texture properties [either frequency and orientation (Experiment 1) or contrast and orientation (Experiment 2)]. The reliability of each cue was controlled by varying the distance over which the change (in frequency, orientation, or contrast) occurred-a kind of "texture blur." In some conditions, the position of the edge signaled by one cue was shifted relative to the other ("perturbation analysis"). An ideal-observer model, previously used in studies of depth perception and color constancy, was fitted to the data. Although the fit can be rejected relative to some more elaborate models, especially given the large quantity of data, this model does account for most trends in the data. A second, suboptimal model that switches between the available cues from trial to trial does a poor job of accounting for the data.

Merging the senses into a robust percept

“…For example, Johnston, Cumming and Landy [17] found that people form a weighted average of motion and disparity signals when asked to report an object's shape. The same is true for texture and disparity signals to depth [18], for the visual perception of slant [19], for the judgment of texture-defined edges [20], and for the estimate of distance [21]. More importantly, Young et al [18], among others, showed that the weights change in the predicted direction as signal reliability is manipulated.…”

“…That is, there should be an observable reduction in variance compared with the individual estimates (see Eqns 3,4). Moreover, the weighting measure alone does not reveal whether the integration of sensory information is optimal because such data are also consistent with a strategy in which an observer bases an answer on only one cue at a time but switches the response to a cue in proportion to its reliability [20,24]. When averaging the answers this 'cue-switching' strategy mimics the performance of cue weighting.…”

“…However, this assumption might not hold in many cases. Landy and Kojima [20], for example, investigated integration of two textural features for the localization of texture-defined edges. Many aspects of their data can be well described by the integration model.…”

Combining Priors and Noisy Visual Cues in a Rapid Pointing Task

“…As has been pointed out previously (Maloney, 2002;Kersten et al, 2004), perceptual tasks are formally identical to hypothesis testing or statistical estimation. There is a large literature on perceptual estimation as an optimal combination of sensory cues, as if observers use ML estimation (Landy et al, 1995;Landy and Kojima, 2001;Ernst and Banks, 2002;Gepshtein and Banks, 2003;Knill and Saunders, 2003;Alais and Burr, 2004;Hillis et al, 2004). Other studies also include a prior probability distribution and hence use MAP estimation as the model of performance.…”

Object Perception as Bayesian Inference

“…The human visual system combines spatial frequency and orientation information optimally when detecting the boundary between two regions (Landy & Kojima 2001). Human observers also behave like an optimal observer when integrating information from skew-symmetry and disparity in perceiving the orientation of a planar object (Saunders & Knill 2001).…”