scholarly journals ‘Proto-rivalry’: how the binocular brain identifies gloss

2016 ◽  
Vol 283 (1830) ◽  
pp. 20160383 ◽  
Author(s):  
Alexander A. Muryy ◽  
Roland W. Fleming ◽  
Andrew E. Welchman
Keyword(s):  
Visual Cortex ◽  
Machine Vision ◽  
Visual System ◽  
Human Visual System ◽  
Binocular Fusion ◽  
Low Level ◽  
Material Appearance

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 particular, we show that the unusual disparity gradients and vertical offsets produced by reflections create distinctive ‘proto-rivalrous’ (barely fusible) image regions that are a critical indicator of gloss. We find that manipulating the gradients and vertical components of binocular disparities yields predictable changes in material appearance. Removing or occluding proto-rivalrous signals makes surfaces look matte, while artificially adding such signals to images makes them appear glossy. This suggests that the human visual system has internalized the idiosyncratic binocular fusion characteristics of glossy surfaces, providing a straightforward means of estimating surface attributes using low-level image signals.

scholarly journals Blindsight and Unconscious Vision: What They Teach Us about the Human Visual System

2016 ◽  
Vol 23 (5) ◽  
pp. 529-541 ◽  
Author(s):  
Sara Ajina ◽  
Holly Bridge
Keyword(s):  
Visual Cortex ◽  
Visual System ◽  
Primary Visual Cortex ◽  
Neural Activity ◽  
Human Visual System ◽  
Visual Information ◽  
Visual Loss ◽  
Physiological Conditions ◽  
The World ◽  
The Brain

Damage to the primary visual cortex removes the major input from the eyes to the brain, causing significant visual loss as patients are unable to perceive the side of the world contralateral to the damage. Some patients, however, retain the ability to detect visual information within this blind region; this is known as blindsight. By studying the visual pathways that underlie this residual vision in patients, we can uncover additional aspects of the human visual system that likely contribute to normal visual function but cannot be revealed under physiological conditions. In this review, we discuss the residual abilities and neural activity that have been described in blindsight and the implications of these findings for understanding the intact system.

Interaction between Transparency and Structure from Motion

1992 ◽  
Vol 4 (4) ◽  
pp. 573-589 ◽  
Author(s):  
Daniel Kersten ◽  
Heinrich H. Bülthoff ◽  
Bennett L. Schwartz ◽  
Kenneth J. Kurtz
Keyword(s):  
Visual System ◽  
Structure From Motion ◽  
Human Visual System ◽  
Binocular Disparity ◽  
Rigid Motion ◽  
Motion Information ◽  
Low Level ◽  
Visual Process ◽  
Pictorial Cues

It is well known that the human visual system can reconstruct depth from simple random-dot displays given binocular disparity or motion information. This fact has lent support to the notion that stereo and structure from motion systems rely on low-level primitives derived from image intensities. In contrast, the judgment of surface transparency is often considered to be a higher-level visual process that, in addition to pictorial cues, utilizes stereo and motion information to separate the transparent from the opaque parts. We describe a new illusion and present psychophysical results that question this sequential view by showing that depth from transparency and opacity can override the bias to see rigid motion. The brain's computation of transparency may involve a two-way interaction with the computation of structure from motion.

scholarly journals The human visual system preserves the hierarchy of 2-dimensional pattern regularity

2021 ◽  
Author(s):  
Peter J. Kohler ◽  
Alasdair D. F. Clarke
Keyword(s):  
Visual Cortex ◽  
Visual System ◽  
Human Visual System ◽  
Posterior Probability ◽  
Brain Activity ◽  
Large Body ◽  
Natural Scenes ◽  
Brain Response ◽  
Lower Amplitude ◽  
Wallpaper Groups

AbstractSymmetries are present at many scales in images of natural scenes. A large body of literature has demonstrated contributions of symmetry to numerous domains of visual perception. The four fundamental symmetries, reflection, rotation, translation and glide reflection, can be combined in exactly 17 distinct ways. These wallpaper groups represent the complete set of symmetries in 2D images and have recently found use in the vision science community as an ideal stimulus set for studying the perception of symmetries in textures. The goal of the current study is to provide a more comprehensive description of responses to symmetry in the human visual system, by collecting both brain imaging (Steady-State Visual Evoked Potentials measured using high-density EEG) and behavioral (symmetry detection thresholds) data using the entire set of wallpaper groups. This allows us to probe the hierarchy of complexity among wallpaper groups, in which simpler groups are subgroups of more complex ones. We find that this hierarchy is preserved almost perfectly in both behavior and brain activity: A multi-level Bayesian GLM indicates that for most of the 63 subgroup relationships, subgroups produce lower amplitude responses in visual cortex (posterior probability: > 0.95 for 56 of 63) and require longer presentation durations to be reliably detected (posterior probability: > 0.95 for 49 of 63). This systematic pattern is seen only in visual cortex and only in components of the brain response known to be symmetric-specific. Our results show that representations of symmetries in the human brain are precise and rich in detail, and that this precision is reflected in behavior. These findings expand our understanding of symmetry perception, and open up new avenues for research on how fine-grained representations of regular textures contribute to natural vision.

scholarly journals The Impact of Dynamic Convergence on the Human Visual System in Head Mounted Displays

2021 ◽  
Author(s):  
◽  
Ryan Sumner
Keyword(s):  
Visual System ◽  
Human Visual System ◽  
Statistical Significance ◽  
Depth Range ◽  
Binocular Fusion ◽  
Virtual Objects ◽  
Beneficial Effects ◽  
Mixed Response ◽  
Head Mounted Displays ◽  
The Impact

<p>The Accommodation-Vergence Conflict (AVC) is a phenomenon in the area of Head-Mounted Displays (HMDs) and one of the key issues hindering the popularity of HMDs largely due to it causing a large number of users to suffer from simulator sickness. There have been several proposed solutions developed by previous researchers, including the introduction of 'Dynamic Convergence' (DC) which, addresses the AVC problem in terms of the vergence depth cue. DC also helps in the performance of binocular fusion when viewing at a close vergence depth. As of yet however, DC has not undergone detailed testing for a number of important cases, which limits the amount of data that has been collected on DC's interaction with the human visual system. In addition, no DC research as of yet has dealt with the effect of a change in vergence depth, and how that change in the vergence angle of the focal plane would effect a user.  Thus, this thesis adds to the growing body of research and knowledge in this field by implementing DC with the addition of some transitions between a change in vergence depth. This is done within the Unity3D game engine in order to further investigate the impact of DC with regard to viewing close virtual objects on HMDs through a number of cases. The added transitions are also tested to see if they have any beneficial effects for users when the vergence angle changes. The investigation is centered around a perception based performance/appreciation-oriented visual study whereby participants were asked about their ability to perform binocular fusion on close virtual objects that were either stationary or moving and varying distances and speeds. Participants were also asked to report any symptoms of discomfort.  The research has adopted a mixed methodology experimental approach by conducting user experiments and surveys, before analysing the results through both in-depth quantitative statistical analysis and a variety of qualitative statistical techniques in order to measure and investigate the scale of the problem associated with the impact of DC on the human visual system in HMDs when viewing close virtual objects.  From the investigation it was confirmed that the approximate effective vergence depth range for DC was 0.3m or less, with statistical significance confirmed at the 0.15m distance. Participants reported having an easier time performing binocular fusion at these closer distances while DC was enabled. As a result of this, the majority of cases and scenarios did not report any significant negative responses in terms of discomfort symptoms. However attempts at improving DC with a transition between vergence depths were met with a mixed response from participants. While the need of a transition way be dependent on the user, there still exists some demand for one, thus it should still be available as an option.</p>

Using Human Visual System modeling for bio-inspired low level image processing

2010 ◽  
Vol 114 (7) ◽  
pp. 758-773 ◽  
Author(s):  
A. Benoit ◽  
A. Caplier ◽  
B. Durette ◽  
J. Herault
Keyword(s):  
Image Processing ◽  
Visual System ◽  
Human Visual System ◽  
System Modeling ◽  
Low Level

scholarly journals Developing a Modified HMAX Model Based on Combined with the Visual Featured Model

2017 ◽  
Vol 7 (3) ◽  
pp. 773
Author(s):  
Yaghoub Pourasad
Keyword(s):  
Visual Cortex ◽  
Object Recognition ◽  
Visual System ◽  
Human Visual System ◽  
Hierarchical Models ◽  
Recognition Rate ◽  
Basic Operation ◽  
High Complexity ◽  
Proposed Model ◽  
Computational Speed

<p>Identify objects based on modeling the human visual system, as an effective method in intelligent identification, has attracted the attention of many researchers. Although the machines have high computational speed but are very weak as compared to humans in terms of diagnosis. Experience has shown that in many areas of image processing, algorithms that have biological backing had more simplicity and better performance. The human visual system, first select the main parts of the image which is provided by the visual featured model, then pays to object recognition which is a hierarchical operations according to this, HMAX model is also provided. HMAX object recognition model from the group of hierarchical models without feedback that its structure and parameters selected based on biological characteristics of the visual cortex. This model is a hierarchical model neural network with four layers, is composed of alternating layers that are simple and complex. Due to the high complexity of the human visual system is virtually impossible to replicate it. For each of the above, separate models have been proposed but in the human visual system, this operation is performed seamlessly, thus, by combining the principles of these models is expected to be closer to the human visual system and obtain a higher recognition rate. In this paper, we introduce an architecture to classify images based on a combination of previous work is based on the basic operation of the visual cortex. According to the results presented, the proposed model compared with the main HMAX model has a much higher recognition rate. Simulations was performed on the database of Caltech101.</p>

Disparity-tuned channels of the human visual system

1993 ◽  
Vol 10 (4) ◽  
pp. 585-596 ◽  
Author(s):  
Lawrence K. Cormack ◽  
Scott B. Stevenson ◽  
Clifton M. Schor
Keyword(s):  
Visual System ◽  
Human Visual System ◽  
Single Unit ◽  
Binocular Disparity ◽  
Random Element ◽  
Stereoscopic Depth ◽  
Detection Thresholds ◽  
Strict Adherence ◽  
Low Level ◽  
Single Unit Recordings

AbstractTraditionally, it has been thought that the processing of binocular disparity for the perception of stereoscopic depth is accomplished via three types of disparity-selective channels – “near,” “far,” and “tuned.” More recent evidence challenges this notion. We have derived disparity-tuning functions psychophysically using a subthreshold summation (i.e. low-level masking) technique. We measured correlation-detection thresholds for dynamic random-element stereograms containing either one or two surfaces in depth. The resulting disparity-tuning functions show an opponent-type profile, indicating the presence of inhibition between disparity-tuned units in the visual system. Moreover, there is clear inhibition between disparities of the same sign, obviating a strict adherence to near-far opponency. These results compare favorably with tuning functions derived psychophysically using an adaptation technique, and with the tuning profiles from published single-unit recordings. Our results suggests a continuum of overlapping disparity-tuned channels, which is consistent with recent physiological evidence as well as models based on other psychophysical data.

Spatiotemporal Isosensitivity Fields in the Human Visual System

Perception ◽  
1986 ◽  
Vol 15 (4) ◽  
pp. 467-472 ◽  
Author(s):  
Bill Jenkins
Keyword(s):  
Visual Cortex ◽  
Visual System ◽  
Human Visual System ◽  
Spatial Domain ◽  
Area 17 ◽  
Dynamic Textures ◽  
Neural Basis ◽  
Global Pattern

The human visual system is capable of detecting correlations, manifested perceptually as global pattern, in mathematically constrained dynamic textures. This ability has given rise to speculation that correlative mechanisms in the human visual system exist and that they have a neural basis similar to the orientationally selective structures discovered in area 17 of the mammalian visual cortex. The limits to the detection of correlation were mapped, spatially and temporally, by means of a psychophysical technique. Evidence is presented that, at least in the spatial domain, the correlation mechanism may be served by a population of such neural units.

scholarly journals Invariance from the Euclidean Geometer's Perspective

Perception ◽  
1994 ◽  
Vol 23 (5) ◽  
pp. 547-561 ◽  
Author(s):  
Luc J Van Gool ◽  
Theo Moons ◽  
Eric Pauwels ◽  
Johan Wagemans
Keyword(s):  
Object Recognition ◽  
Machine Vision ◽  
Visual System ◽  
Human Visual System ◽  
State Of The Art ◽  
The State ◽  
The Other ◽  
Other Hand ◽  
Trade Offs ◽  
Basic Philosophy

It is remarkable how well the human visual system can cope with changing viewpoints when it comes to recognising shapes. The state of the art in machine vision is still quite remote from solving such tasks. Nevertheless, a surge in invariance-based research has led to the development of methods for solving recognition problems still considered hard until recently. A nonmathematical account explains the basic philosophy and trade-offs underlying this strand of research. The principles are explained for the relatively simple case of planar-object recognition under arbitrary viewpoints. Well-known Euclidean concepts form the basis of invariance in this case. Introducing constraints in addition to that of planarity may further simplify the invariants. On the other hand, there are problems for which no invariants exist.

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