scholarly journals Biologically Inspired Hierarchical Contour Detection with Surround Modulation and Neural Connection

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2559 ◽  
Author(s):  
Shuai Li ◽  
Yuelei Xu ◽  
Wei Cong ◽  
Shiping Ma ◽  
Mingming Zhu ◽  
...  
Keyword(s):  
Receptive Fields ◽  
Contour Detection ◽  
Biologically Inspired ◽  
Neural Connections ◽  
Detection Model ◽  
Complex Scenes ◽  
Image Contour ◽  
Conventional Models ◽  
Perceptual Characteristics ◽  
Processing Mechanisms

Contour is a very important feature in biological visual cognition and has been extensively investigated as a fundamental vision problem. In connection with the limitations of conventional models in detecting image contours in complex scenes, a hierarchical image contour extraction method is proposed based on the biological vision mechanism that draws on the perceptual characteristics of the early vision for features such as edges, shapes, and colours. By simulating the information processing mechanisms of the cells’ receptive fields in the early stages of the biological visual system, we put forward a computational model that combines feedforward, lateral, and feedback neural connections to decode and obtain the image contours. Our model simulations and their results show that the established hierarchical contour detection model can adequately fit the characteristics of the biological experiment, quickly and effectively detect the salient contours in complex scenes, and better suppress the unwanted textures.

scholarly journals A Biologically-Inspired Framework for Contour Detection Using Superpixel-Based Candidates and Hierarchical Visual Cues

Sensors ◽  
2015 ◽  
Vol 15 (10) ◽  
pp. 26654-26674 ◽  
Author(s):  
Xiao Sun ◽  
Ke Shang ◽  
Delie Ming ◽  
Jinwen Tian ◽  
Jiayi Ma
Keyword(s):  
Visual Cues ◽  
Contour Detection ◽  
Biologically Inspired

scholarly journals Biologically inspired computational modeling of motion based on middle temporal area

2018 ◽  
Vol 9 (1) ◽  
pp. 60-71 ◽  
Author(s):  
Fernanda da C. e C. Faria ◽  
Jorge Batista ◽  
Helder Araújo
Keyword(s):  
Receptive Fields ◽  
Biologically Inspired ◽  
Significant Information ◽  
Temporal Area ◽  
Middle Temporal ◽  
Complex Cells ◽  
Motion Computation ◽  
Reciprocal Connections ◽  
Middle Temporal Area ◽  
Last Stage

Abstract This paper describes a bio-inspired algorithm for motion computation based on V1 (Primary Visual Cortex) andMT (Middle Temporal Area) cells. The behavior of neurons in V1 and MT areas contain significant information to understand the perception of motion. From a computational perspective, the neurons are treated as two dimensional filters to represent the receptive fields of simple cells that compose the complex cells. A modified elaborated Reichardt detector, adding an output exponent before the last stage followed by a re-entry stage of modulating feedback from MT, (reciprocal connections of V1 and MT) in a hierarchical framework, is proposed. The endstopped units, where the receptive fields of cells are surrounded by suppressive regions, are modeled as a divisive operation. MT cells play an important role for integrating and interpreting inputs from earlier-level (V1).We fit a normalization and a pooling to find the most active neurons for motion detection. All steps employed are physiologically inspired processing schemes and need some degree of simplification and abstraction. The results suggest that our proposed algorithm can achieve better performance than recent state-of-the-art bio-inspired approaches for real world images.

scholarly journals A New Discovery on Visual Information Dynamic Changes from Retina to V2

2021 ◽  
Author(s):  
Haixin Zhong ◽  
Rubin Wang
Keyword(s):  
Visual Information ◽  
Receptive Fields ◽  
Small Degree ◽  
Image Features ◽  
The Rich ◽  
Information Dynamic ◽  
Dynamic Degradation ◽  
Neural Mapping ◽  
Processing Mechanisms ◽  
New Discovery

Abstract The information processing mechanisms of the visual nervous system remain to be unsolved scientific issues in neuroscience field, owing to a lack of unified and widely accepted theory for explanation. It has been well documented that approximately 80% of the rich and complicated perceptual information from the real world is transmitted to the visual cortex, only a small fraction of visual information reaches the V1 area. This, nevertheless, does not affect our visual perception. Furthermore, how neurons in V2 encode such a small amount of visual information has yet to be addressed. To this end, the current paper establishes a visual network model for retina-LGN-V1-V2 and quantitatively accounts for that response to the scarcity of visual information and encoding rules, based on the principle of neural mapping from V1 to V2. The results demonstrate that the visual information has a small degree of dynamic degradation when it is mapped from V1 to V2, during which there is a convolution calculation occurring. Therefore, visual information dynamic degradation mainly manifests itself along the pathway of the retina to V1, rather than V1 to V2. The slight changes in the visual information are attributable to the fact that the receptive fields (RFs) of V2 cannot further extract the image features. Meanwhile, despite the scarcity of visual information mapped from the retina, the RFs of V2 can still accurately respond to and encode “corner” information, due to the effects of synaptic plasticity, of which function is not existed in V1. This is a new discovery that has never been noticed before. To sum up, the coding of the “contour” feature (edge and corner) is achieved in the pathway of retina-LGN-V1-V2.

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