scholarly journals Computational modeling of the neural representation of object shape in the primate ventral visual system

2015 ◽  
Vol 9 ◽  
Author(s):  
Akihiro Eguchi ◽  
Bedeho M. W. Mender ◽  
Benjamin D. Evans ◽  
Glyn W. Humphreys ◽  
Simon M. Stringer
Keyword(s):  
Visual System ◽  
Computational Modeling ◽  
Neural Representation ◽  
Object Shape ◽  
Ventral Visual System

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.

Action relationships concatenate representations of separate objects in the ventral visual system

NeuroImage ◽  
2010 ◽  
Vol 52 (4) ◽  
pp. 1541-1548 ◽  
Author(s):  
Katherine L. Roberts ◽  
Glyn W. Humphreys
Keyword(s):  
Visual System ◽  
Ventral Visual System

scholarly journals The neural representation of the gender of faces in the primate visual system: A computer modeling study.

2017 ◽  
Vol 124 (2) ◽  
pp. 154-167
Author(s):  
Thomas Minot ◽  
Hannah L. Dury ◽  
Akihiro Eguchi ◽  
Glyn W. Humphreys ◽  
Simon M. Stringer
Keyword(s):  
Visual System ◽  
Computer Modeling ◽  
Neural Representation ◽  
System A ◽  
Modeling Study

2D and 3D Visual Attention for Computer Vision

2016 ◽  
pp. 1-44
Author(s):  
Vincent Ricordel ◽  
Junle Wang ◽  
Matthieu Perreira Da Silva ◽  
Patrick Le Callet
Keyword(s):  
Computer Vision ◽  
Visual Perception ◽  
Visual Attention ◽  
Visual System ◽  
Computational Modeling ◽  
Depth Perception ◽  
Human Visual System ◽  
Visual Importance ◽  
2D And 3D ◽  
The Impact

Visual attention is one of the most important mechanisms deployed in the human visual system (HVS) to reduce the amount of information that our brain needs to process. An increasing amount of efforts has been dedicated to the study of visual attention, and this chapter proposes to clarify the advances achieved in computational modeling of visual attention. First the concepts of visual attention, including the links between visual salience and visual importance, are detailed. The main characteristics of the HVS involved in the process of visual perception are also explained. Next we focus on eye-tracking, because of its role in the evaluation of the performance of the models. A complete state of the art in computational modeling of visual attention is then presented. The research works that extend some visual attention models to 3D by taking into account of the impact of depth perception are finally explained and compared.

scholarly journals Holistic neural coding of Chinese character forms in bilateral ventral visual system

2015 ◽  
Vol 141 ◽  
pp. 28-34 ◽  
Author(s):  
Ce Mo ◽  
Mengxia Yu ◽  
Carol Seger ◽  
Lei Mo
Keyword(s):  
Visual System ◽  
Neural Coding ◽  
Chinese Character ◽  
Ventral Visual System

scholarly journals Object Recognition Inspiring HVS

2018 ◽  
Vol 12 (2) ◽  
pp. 783
Author(s):  
Mohammadesmaeil Akbarpour ◽  
Nasser Mehrshad ◽  
Seyyed-Mohammad Razavi
Keyword(s):  
Object Recognition ◽  
Visual System ◽  
Computational Modeling ◽  
Hierarchical Model ◽  
Human Visual System ◽  
Visual Pathway ◽  
Natural Images ◽  
Significant Performance

<p><span>Human recognize objects in complex natural images very fast within a fraction of a second. Many computational object recognition models inspired from this powerful ability of human. The Human Visual System (HVS) recognizes object in several processing layers which we know them as hierarchically model. Due to amazing complexity of HVS and the connections in visual pathway, computational modeling of HVS directly from its physiology is not possible. So it considered as a some blocks and each block modeled separately. One models inspiring of HVS is HMAX which its main problem is selecting patches in random way. As HMAX is a hierarchical model, HMAX can enhanced with enhancing each layer separately. In this paper instead of random patch extraction, Desirable Patches for HMAX (DPHMAX) will extracted.  HVS for extracting patch first selected patches with more information. For simulating this block patches with more variance will be selected. Then HVS will chose patches with more similarity in a class. For simulating this block one algorithm is used. For evaluating proposed method, Caltech 5 and Caltech101 datasets are used. Results show that the proposed method (DPMAX) provides a significant performance over HMAX and other models with the same framework.</span></p>

scholarly journals The Variable Role of Abstraction in the Neural Representation of Categories in the Visual System

2017 ◽  
Vol 17 (10) ◽  
pp. 1233
Author(s):  
J. Brendan Ritchie ◽  
Hans Op de Beeck
Keyword(s):  
Visual System ◽  
Neural Representation
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