scholarly journals Special issue Visual system and image technology. 4. Neural mechanisms of information processing and recognizing. 1. Neural mechanisms of pattern vision.

1986 ◽  
Vol 40 (4) ◽  
pp. 256-266
Author(s):  
Hide-aki Saito
Keyword(s):  
Information Processing ◽  
Visual System ◽  
Neural Mechanisms ◽  
Special Issue ◽  
Pattern Vision

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.

Visual control of orientation behaviour in the fly: Part I. A quantitative analysis

1976 ◽  
Vol 9 (3) ◽  
pp. 311-375 ◽  
Author(s):  
Werner Reichardt ◽  
Tomaso Poggio
Keyword(s):  
Information Processing ◽  
Quantitative Analysis ◽  
Visual System ◽  
Sensory Information ◽  
Model Systems ◽  
Suitable Model ◽  
Logical Organization ◽  
Integrative Level ◽  
Processing Properties ◽  
Different Levels

An understanding of sensory information processing in the nervous system will probably require investigations with a variety of ‘model’ systems at different levels of complexity.Our choice of a suitable model system was constrained by two conflicting requirements: on one hand the information processing properties of the system should be rather complex, on the other hand the system should be amenable to a quantitative analysis. In this sense the fly represents a compromise.In these two papers we explore how optical information is processed by the fly's visual system. Our objective is to unravel the logical organization of the fly's visual system and its underlying functional and computational principles. Our approach is at a highly integrative level. There are different levels of analysing and ‘understanding’ complex systems, like a brain or a sophisticated computer.

Man, Monkey, and Blindsight

1998 ◽  
Vol 4 (4) ◽  
pp. 227-230 ◽  
Author(s):  
Tirin Moore ◽  
Hillary R. Rodman ◽  
Charles G. Gross
Keyword(s):  
Visual Cortex ◽  
Visual System ◽  
Primary Visual Cortex ◽  
Visual Function ◽  
Forced Choice ◽  
Neural Mechanisms ◽  
Residual Vision ◽  
Choice Testing

The visual function that survives damage to the primary visual cortex (V1) in humans is often unaccompanied by awareness. This type of residual vision, called “blindsight,” has raised considerable interest because it implies a separation of conscious from unconscious vision mechanisms. The monkey visual system has proven to be a useful model in elucidating the possible neural mechanisms of residual vision and blindsight in humans. Clear similarities, however, between the phenomenology of human and monkey residual vision have only recently become evident. This article summarizes parallels between residual vision in monkeys and humans with damage to V1. These parallels Include the tendency of the remaining vision to require forced-choice testing and the fact that more robust residual vision remains when V1 damage is sustained early in life. NEUROSCIENTIST 4:227–230

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