Alexander A. Muryy

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Binocular stereopsis is a powerful visual depth cue. To exploit it, the brain matches features from the two eyes' views and measures their interocular disparity. This works well for matte surfaces because disparities indicate true surface locations. However, specular (glossy) surfaces are problematic because highlights and reflections are displaced from the(More)
Because specular reflection is view-dependent, shiny surfaces behave radically differently from matte, textured surfaces when viewed with two eyes. As a result, specular reflections pose substantial problems for binocular stereopsis. Here we use a combination of computer graphics and geometrical analysis to characterize the key respects in which specular(More)
Recovering 3D scenes from 2D images is an under-constrained task; optimal estimation depends upon knowledge of the underlying scene statistics. Here we introduce the Southampton-York Natural Scenes dataset (SYNS:, which provides comprehensive scene statistics useful for understanding biological vision and for improving machine(More)
Visually identifying glossy surfaces can be crucial for survival (e.g. ice patches on a road), yet estimating gloss is computationally challenging for both human and machine vision. Here, we demonstrate that human gloss perception exploits some surprisingly simple binocular fusion signals, which are likely available early in the visual cortex. In(More)
The visual impression of an object's surface reflectance ("gloss") relies on a range of visual cues, both monocular and binocular. Whereas previous imaging work has identified processing within ventral visual areas as important for monocular cues, little is known about cortical areas involved in processing binocular cues. Here, we used human functional MRI(More)
Surface gloss information conveyed by image cues (i.e., highlights) has been shown to be processed in ventral and dorsal areas. In this study we used fMRI to distinguish the brain areas that selectively process 2D and 3D cues about surface gloss. We performed one experiment using 2D images of random objects with glossy surfaces where diffuse highlights(More)
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Humans are adept at estimating 3D scene geometry from a stereo image pair, or even from a single image. Computer vision algorithms are less good. Gaining traction on this problem requires a dataset that contains good quality images and ground truth data, and represents the complex and diverse scenes that we encounter. To this end we have developed the(More)
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