scholarly journals Special issue Visual system and image technology. 4. Neural mechanisms of information processing and recognizing. 2. Motion perception and eye movement.

1986 ◽  
Vol 40 (4) ◽  
pp. 266-273
Author(s):  
Minoru Yasuda ◽  
Tadahiko Fukuda
Keyword(s):  
Information Processing ◽  
Visual System ◽  
Eye Movement ◽  
Motion Perception ◽  
Neural Mechanisms ◽  
Special Issue

Mechanisms of human motion perception revealed by a new cyclopean illusion

Science ◽  
1986 ◽  
Vol 232 (4746) ◽  
pp. 95-97 ◽  
Author(s):  
M Shadlen ◽  
T Carney
Keyword(s):  
Visual System ◽  
Motion Perception ◽  
Direction Selectivity ◽  
Human Motion ◽  
Neural Mechanisms ◽  
The Other ◽  
Temporal Phase ◽  
Other Hand ◽  
Hand Image ◽  
Over Time

A new cyclopean illusion of motion may bear on neural mechanisms of direction selectivity. Stationary flickering patterns were presented to each eye, and the resulting fused pattern was perceived to be moving. To determine direction of motion, the visual system seems to integrate image components differing by 90 degrees in spatial and temporal phase. On the other hand, image speed seems to be derived from displacement of features over time. A model of neural direction selectivity is discussed in light of these results.

scholarly journals Structured Computational Modeling of Human Visual System for No-reference Image Quality Assessment

2021 ◽  
Author(s):  
Wen-Han Zhu ◽  
Wei Sun ◽  
Xiong-Kuo Min ◽  
Guang-Tao Zhai ◽  
Xiao-Kang Yang
Keyword(s):  
Information Processing ◽  
Image Quality ◽  
Visual System ◽  
Computational Modeling ◽  
Quality Assessment ◽  
Human Visual System ◽  
Visual Communication ◽  
Visual Experience ◽  
Image Quality Assessment ◽  
Model Based

AbstractObjective image quality assessment (IQA) plays an important role in various visual communication systems, which can automatically and efficiently predict the perceived quality of images. The human eye is the ultimate evaluator for visual experience, thus the modeling of human visual system (HVS) is a core issue for objective IQA and visual experience optimization. The traditional model based on black box fitting has low interpretability and it is difficult to guide the experience optimization effectively, while the model based on physiological simulation is hard to integrate into practical visual communication services due to its high computational complexity. For bridging the gap between signal distortion and visual experience, in this paper, we propose a novel perceptual no-reference (NR) IQA algorithm based on structural computational modeling of HVS. According to the mechanism of the human brain, we divide the visual signal processing into a low-level visual layer, a middle-level visual layer and a high-level visual layer, which conduct pixel information processing, primitive information processing and global image information processing, respectively. The natural scene statistics (NSS) based features, deep features and free-energy based features are extracted from these three layers. The support vector regression (SVR) is employed to aggregate features to the final quality prediction. Extensive experimental comparisons on three widely used benchmark IQA databases (LIVE, CSIQ and TID2013) demonstrate that our proposed metric is highly competitive with or outperforms the state-of-the-art NR IQA measures.

Information Processing vs Visual Attention during Incidental Learning

1976 ◽  
Vol 43 (2) ◽  
pp. 555-561 ◽  
Author(s):  
Richard A. Wyrick ◽  
Vincent J. Tempone ◽  
Jack Capehart
Keyword(s):  
Information Processing ◽  
Individual Differences ◽  
Visual Attention ◽  
Eye Movement ◽  
Discrimination Training ◽  
Incidental Learning ◽  
Intentional Learning ◽  
Order Information ◽  
Level Of Training ◽  
The Relationship

The relationship between attention and incidental learning during discrimination training was studied in 30 children, aged 10 to 11. A polymetric eye-movement recorder measured direct visual attention. Consistent with previous findings, recall of incidental stimuli was greatest during the initial and terminal stages of intentional learning. Contrary to previous explanations, however, visual attention to incidental stimuli was not related to training. While individual differences in attention to incidental stimuli were predictive of recall, attention to incidental stimuli was not related to level of training. Results suggested that changes in higher order information processing rather than direct visual attention were responsible for the curvilinear learning of incidental stimuli during intentional training.

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