scholarly journals A simple photometric factor in perceived depth order of bistable transparency patterns

2014 ◽  
Vol 14 (5) ◽  
pp. 2-2 ◽  
Author(s):  
T. Fukiage ◽  
T. Oishi ◽  
K. Ikeuchi
Keyword(s):  
Depth Order

scholarly journals Second-order motion conveys depth-order information

2004 ◽  
Vol 4 (10) ◽  
pp. 1 ◽  
Author(s):  
Jay Hegdé ◽  
Thomas D. Albright ◽  
Gene R. Stoner
Keyword(s):  
Second Order ◽  
Order Information ◽  
Depth Order

scholarly journals The relation between disparity and velocity signals of rigidly moving objects constrains depth order perception

2007 ◽  
Vol 47 (10) ◽  
pp. 1335-1349 ◽  
Author(s):  
Massimiliano Di Luca ◽  
Fulvio Domini ◽  
Corrado Caudek
Keyword(s):  
Moving Objects ◽  
Depth Order

scholarly journals Integrating multiple cues to depth order at object boundaries

2011 ◽  
Vol 73 (7) ◽  
pp. 2218-2235 ◽  
Author(s):  
Ellen C. Hildreth ◽  
Constance S. Royden
Keyword(s):  
Multiple Cues ◽  
Depth Order

scholarly journals Border-ownership-dependent tilt aftereffect for shape defined by binocular disparity and motion parallax

2018 ◽  
Author(s):  
Reuben Rideaux ◽  
William J Harrison
Keyword(s):  
Binocular Disparity ◽  
Motion Parallax ◽  
Neural Systems ◽  
Visual Object ◽  
Access To Information ◽  
Tilt Aftereffect ◽  
Critical Function ◽  
Depth Order ◽  
Depth Cues ◽  
Ground Segmentation

ABSTRACTDiscerning objects from their surrounds (i.e., figure-ground segmentation) in a way that guides adaptive behaviours is a fundamental task of the brain. Neurophysiological work has revealed a class of cells in the macaque visual cortex that may be ideally suited to support this neural computation: border-ownership cells (Zhou, Friedman, & von der Heydt, 2000). These orientation-tuned cells appear to respond conditionally to the borders of objects. A behavioural correlate supporting the existence of these cells in humans was demonstrated using two-dimensional luminance defined objects (von der Heydt, Macuda, & Qiu, 2005). However, objects in our natural visual environments are often signalled by complex cues, such as motion and depth order. Thus, for border-ownership systems to effectively support figure-ground segmentation and object depth ordering, they must have access to information from multiple depth cues with strict depth order selectivity. Here we measure in humans (of both sexes) border-ownership-dependent tilt aftereffects after adapting to figures defined by either motion parallax or binocular disparity. We find that both depth cues produce a tilt aftereffect that is selective for figure-ground depth order. Further, we find the effects of adaptation are transferable between cues, suggesting that these systems may combine depth cues to reduce uncertainty (Bülthoff & Mallot, 1988). These results suggest that border-ownership mechanisms have strict depth order selectivity and access to multiple depth cues that are jointly encoded, providing compelling psychophysical support for their role in figure-ground segmentation in natural visual environments.SIGNIFICANCE STATEMENTSegmenting a visual object from its surrounds is a critical function that may be supported by “border-ownership” neural systems that conditionally respond to object borders. Psychophysical work indicates these systems are sensitive to objects defined by luminance contrast. To effectively support figure-ground segmentation, however, neural systems supporting border-ownership must have access to information from multiple depth cues and depth order selectivity. We measured border-ownership-dependent tilt aftereffects to figures defined by either motion parallax or binocular disparity and found aftereffects for both depth cues. These effects were transferable between cues, but selective for figure-ground depth order. Our results suggest that the neural systems supporting figure-ground segmentation have strict depth order selectivity and access to multiple depth cues that are jointly encoded.

scholarly journals The Role of Temporal 'Priors' in the Perception of Depth-Order from Motion: A Priming Study

2010 ◽  
Vol 10 (7) ◽  
pp. 48-48
Author(s):  
A. Epting ◽  
J. Hegde
Keyword(s):  
Depth Order

scholarly journals Emergence of border-ownership by large-scale consistency and long-range interactions: Neuro-computational model to reflect global configurations

2021 ◽  
Author(s):  
Naoki Kogo ◽  
Vicky Froyen
Keyword(s):  
Surface Properties ◽  
Large Scale ◽  
Computational Models ◽  
Depth Order ◽  
Global Configuration ◽  
Long Range Interactions ◽  
The Brain ◽  
Computational Processes ◽  
Advanced Version ◽  
Ground Organization

The visual system performs remarkably well to perceive depth order of surfaces without stereo disparity, indicating the importance of figure-ground organization based on pictorial cues. To understand how figure-ground organization emerges, it is essential to investigate how the global configuration of an image is reflected. In the past, many neuro-computational models developed to reproduce figure-ground organization implemented algorithms to give a bias to convex areas. However, in certain conditions, a convex area can be perceived as a hole and a non-convex area as figural. This occurs when the surface properties of the convex area are consistent with the background and, hence, are grouped together in our perception. We argue that large-scale consistency of surface properties is reflected in the border-ownership computation. We developed a model, called DISC2, that first analyzes relationships between two border-ownership signals of all possible combinations in the image. It then enhances signals if they satisfy the following conditions: 1. the two signals fit to a convex configuration, and 2. the surface properties at the locations of the two signals are consistent. The strength of the enhancement decays with distance between the signals. The model gives extremely robust responses to various images with complexities both in shape and depth order. Furthermore, we developed an advanced version of the model ("augmented model") where the global computation above interacts with local computation of curvilinearity, which further enhanced the robust nature of the model. The results suggest the involvement of similar computational processes in the brain for figure-ground organization.

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