Human visual system model

About: Human visual system model is a research topic. Over the lifetime, 8697 publications have been published within this topic receiving 259440 citations.

Face Processing in Humans is Compatible with a Simple Shape-Based Model of Vision

Abstract: Understanding how the human visual system recognizes objects is one of the key challenges in neuroscience. Inspired by a large body of physiological evidence, a general class of recognition models has emerged, which is based on a hierarchical organization of visual processing, with succeeding stages being sensitive to image features of increasing complexity. However, these models appear to be incompatible with some well-known psychophysical results. Prominent among these are experiments investigating recognition impairments caused by vertical inversion of images, especially those of faces. It has been reported that faces that differ 'featurally' are much easier to distinguish when inverted than those that differ 'configurally'; a finding that is difficult to reconcile with the physiological models. Here, we show that after controlling for subjects' expectations, there is no difference between 'featurally' and 'configurally' transformed faces in terms of inversion effect. This result reinforces the plausibility of simple hierarchical models of object representation and recognition in the cortex.

Perceiving illumination inconsistencies in scenes.

Abstract: The human visual system is adept at detecting and encoding statistical regularities in its spatiotemporal environment. Here, we report an unexpected failure of this ability in the context of perceiving inconsistencies in illumination distributions across a scene. Prior work with arrays of objects all having uniform reflectance has shown that one inconsistently illuminated target can 'pop out' among a field of consistently illuminated objects (eg Enns and Rensink, 1990 Science 247 721 723; Sun and Perona, 1997 Perception 26 519-529). In these studies, the luminance pattern of the odd target could be interpreted as arising from either an inconsistent illumination or inconsistent pigmentation of the target. Either cue might explain the rapid detection. In contrast, we find that once the geometrical regularity of the previous displays is removed, the visual system is remarkably insensitive to illumination inconsistencies, both in experimental stimuli and in altered images of real scenes. Whether the target is interpreted as oddly illuminated or oddly pigmented, it is very difficult to find if the only cue is deviation from the regularity of illumination or reflectance. Our results allow us to draw inferences about how the visual system encodes illumination distributions across scenes. Specifically, they suggest that the visual system does not verify the global consistency of locally derived estimates of illumination direction.

Empirical Validation of the Saliency-based Model of Visual Attention

Abstract: Visual attention is the ability of the human vision system to detect salient parts of the scene, on which higher vision tasks, such as recognition, can focus. In human vision, it is believed that visual attention is intimately linked to the eye movements and that the fixation points correspond to the location of the salient scene parts. In computer vision, the paradigm of visual attention has been widely investigated and a saliency-based model of visual attention is now available that is commonly accepted and used in the field, despite the fact that its biological grounding has not been fully assessed. This work proposes a new method for quantitatively assessing the plausibility of this model by comparing its performance with human behavior. The basic idea is to compare the map of attention - the saliency map - produced by the computational model with a fixation density map derived from eye movement experiments. This human attention map can be constructed as an integral of single impulses located at the positions of the successive fixation points. The resulting map has the same format as the computer-generated map, and can easily be compared by qualitative and quantitative methods. Some illustrative examples using a set of natural and synthetic color images show the potential of the validation method to assess the plausibility of the attention model.

A Nonlinear Model for the Spatial Characteristics of the Human Visual System

Abstract: Several recent papers have presented data from experimental investigations of the human ivsual system (HVS) which support the general hypothesis that the HVS is composed of spatial frequency channels. It has been pointed out, however, that a linear systems analysis of the entire system is not valid. Furthermore, a nonlinear model consisting of a log-bandpass filter produced some experimental results with deviations at high spatial frequencies. A new structure for a nonlinear mathematical model which is easily quantifiable, produces results which compare with experimental data, and has a physiological correlate is presented. The significance of this model is that the bandwidth of the visual system decreases as contrast increases. Thus the system appears to maximize the signal to noise ratio while attempting to maintain a constant " perceptual" spatial-frequency fidelity.