The Mechanism of Orientation Detection Based on Artificial Visual System for Greyscale Images
Human visual system is a crucial component of the nervous system, enabling us to perceive and understand the surrounding world. Advancements in research on the visual system have profound implications for our understanding of both biological and computer vision. Orientation detection, a fundamental...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/11/12/2715 |
| _version_ | 1797593640063729664 |
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| author | Xiliang Zhang Sichen Tao Zheng Tang Shuxin Zheng Yoki Todo |
| author_facet | Xiliang Zhang Sichen Tao Zheng Tang Shuxin Zheng Yoki Todo |
| author_sort | Xiliang Zhang |
| collection | DOAJ |
| description | Human visual system is a crucial component of the nervous system, enabling us to perceive and understand the surrounding world. Advancements in research on the visual system have profound implications for our understanding of both biological and computer vision. Orientation detection, a fundamental process in the visual cortex where neurons respond to linear stimuli in specific orientations, plays a pivotal role in both fields. In this study, we propose a novel orientation detection mechanism for local neurons based on dendrite computation, specifically designed for grayscale images. Our model comprises eight neurons capable of detecting local orientation information, with inter-neuronal interactions facilitated through nonlinear dendrites. Through the extraction of local orientation information, this mechanism effectively derives global orientation information, as confirmed by successful computer simulations. Experimental results demonstrate that our mechanism exhibits remarkable orientation detection capabilities irrespective of variations in size, shape, or position, which aligns with previous physiological research findings. These findings contribute to our understanding of the human visual system and provide valuable insights into both biological and computer vision. The proposed orientation detection mechanism, with its nonlinear dendritic computations, offers a promising approach for improving orientation detection in grayscale images. |
| first_indexed | 2024-03-11T02:11:08Z |
| format | Article |
| id | doaj.art-3255947315ca470fae6fbf9e720927a2 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-11T02:11:08Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-3255947315ca470fae6fbf9e720927a22023-11-18T11:28:47ZengMDPI AGMathematics2227-73902023-06-011112271510.3390/math11122715The Mechanism of Orientation Detection Based on Artificial Visual System for Greyscale ImagesXiliang Zhang0Sichen Tao1Zheng Tang2Shuxin Zheng3Yoki Todo4Faculty of Engineering, University of Toyama, Toyama-shi 930-8555, JapanFaculty of Engineering, University of Toyama, Toyama-shi 930-8555, JapanFaculty of Engineering, University of Toyama, Toyama-shi 930-8555, JapanSchool of Economics and Business, Changzhou Vocational Institute of Textile and Garment, Changzhou 213164, ChinaFaculty of Electrical and Computer Engineering, Kanazawa University, Kanazawa-shi 920-1192, JapanHuman visual system is a crucial component of the nervous system, enabling us to perceive and understand the surrounding world. Advancements in research on the visual system have profound implications for our understanding of both biological and computer vision. Orientation detection, a fundamental process in the visual cortex where neurons respond to linear stimuli in specific orientations, plays a pivotal role in both fields. In this study, we propose a novel orientation detection mechanism for local neurons based on dendrite computation, specifically designed for grayscale images. Our model comprises eight neurons capable of detecting local orientation information, with inter-neuronal interactions facilitated through nonlinear dendrites. Through the extraction of local orientation information, this mechanism effectively derives global orientation information, as confirmed by successful computer simulations. Experimental results demonstrate that our mechanism exhibits remarkable orientation detection capabilities irrespective of variations in size, shape, or position, which aligns with previous physiological research findings. These findings contribute to our understanding of the human visual system and provide valuable insights into both biological and computer vision. The proposed orientation detection mechanism, with its nonlinear dendritic computations, offers a promising approach for improving orientation detection in grayscale images.https://www.mdpi.com/2227-7390/11/12/2715artificial visual systemorientation detectiondendritic neuron modelconvolutional neural networknoise resistance Greyscale Images |
| spellingShingle | Xiliang Zhang Sichen Tao Zheng Tang Shuxin Zheng Yoki Todo The Mechanism of Orientation Detection Based on Artificial Visual System for Greyscale Images Mathematics artificial visual system orientation detection dendritic neuron model convolutional neural network noise resistance Greyscale Images |
| title | The Mechanism of Orientation Detection Based on Artificial Visual System for Greyscale Images |
| title_full | The Mechanism of Orientation Detection Based on Artificial Visual System for Greyscale Images |
| title_fullStr | The Mechanism of Orientation Detection Based on Artificial Visual System for Greyscale Images |
| title_full_unstemmed | The Mechanism of Orientation Detection Based on Artificial Visual System for Greyscale Images |
| title_short | The Mechanism of Orientation Detection Based on Artificial Visual System for Greyscale Images |
| title_sort | mechanism of orientation detection based on artificial visual system for greyscale images |
| topic | artificial visual system orientation detection dendritic neuron model convolutional neural network noise resistance Greyscale Images |
| url | https://www.mdpi.com/2227-7390/11/12/2715 |
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