A Motion-Direction-Detecting Model for Gray-Scale Images Based on the Hassenstein–Reichardt Model
The visual system of sighted animals plays a critical role in providing information about the environment, including motion details necessary for survival. Over the past few years, numerous studies have explored the mechanism of motion direction detection in the visual system for binary images, incl...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-05-01
|
| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/12/11/2481 |
| _version_ | 1797597676402900992 |
|---|---|
| author | Zhiyu Qiu Yuki Todo Chenyang Yan Zheng Tang |
| author_facet | Zhiyu Qiu Yuki Todo Chenyang Yan Zheng Tang |
| author_sort | Zhiyu Qiu |
| collection | DOAJ |
| description | The visual system of sighted animals plays a critical role in providing information about the environment, including motion details necessary for survival. Over the past few years, numerous studies have explored the mechanism of motion direction detection in the visual system for binary images, including the Hassenstein–Reichardt model (HRC model) and the HRC-based artificial visual system (AVS). In this paper, we introduced a contrast-response system based on previous research on amacrine cells in the visual system of <i>Drosophila</i> and other species. We combined this system with the HRC-based AVS to construct a motion-direction-detection system for gray-scale images. Our experiments verified the effectiveness of our model in detecting the motion direction in gray-scale images, achieving at least 99% accuracy in all experiments and a remarkable 100% accuracy in several circumstances. Furthermore, we developed two convolutional neural networks (CNNs) for comparison to demonstrate the practicality of our model. |
| first_indexed | 2024-03-11T03:08:55Z |
| format | Article |
| id | doaj.art-c08e942842474b8fb557a69e83c5ec34 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-11T03:08:55Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-c08e942842474b8fb557a69e83c5ec342023-11-18T07:45:29ZengMDPI AGElectronics2079-92922023-05-011211248110.3390/electronics12112481A Motion-Direction-Detecting Model for Gray-Scale Images Based on the Hassenstein–Reichardt ModelZhiyu Qiu0Yuki Todo1Chenyang Yan2Zheng Tang3Faculty of Electrical and Computer Engineering, Kanazawa University, Kakuma-Machi, Kanazawa 920-1192, JapanFaculty of Electrical and Computer Engineering, Kanazawa University, Kakuma-Machi, Kanazawa 920-1192, JapanFaculty of Electrical and Computer Engineering, Kanazawa University, Kakuma-Machi, Kanazawa 920-1192, JapanDepartment of Intelligence Information Systems, University of Toyama, 3190 Gofuku, Toyama 930-8555, JapanThe visual system of sighted animals plays a critical role in providing information about the environment, including motion details necessary for survival. Over the past few years, numerous studies have explored the mechanism of motion direction detection in the visual system for binary images, including the Hassenstein–Reichardt model (HRC model) and the HRC-based artificial visual system (AVS). In this paper, we introduced a contrast-response system based on previous research on amacrine cells in the visual system of <i>Drosophila</i> and other species. We combined this system with the HRC-based AVS to construct a motion-direction-detection system for gray-scale images. Our experiments verified the effectiveness of our model in detecting the motion direction in gray-scale images, achieving at least 99% accuracy in all experiments and a remarkable 100% accuracy in several circumstances. Furthermore, we developed two convolutional neural networks (CNNs) for comparison to demonstrate the practicality of our model.https://www.mdpi.com/2079-9292/12/11/2481artificial visual systemamacrine cellsmechanismneuronmotion detectiongray-scale |
| spellingShingle | Zhiyu Qiu Yuki Todo Chenyang Yan Zheng Tang A Motion-Direction-Detecting Model for Gray-Scale Images Based on the Hassenstein–Reichardt Model Electronics artificial visual system amacrine cells mechanism neuron motion detection gray-scale |
| title | A Motion-Direction-Detecting Model for Gray-Scale Images Based on the Hassenstein–Reichardt Model |
| title_full | A Motion-Direction-Detecting Model for Gray-Scale Images Based on the Hassenstein–Reichardt Model |
| title_fullStr | A Motion-Direction-Detecting Model for Gray-Scale Images Based on the Hassenstein–Reichardt Model |
| title_full_unstemmed | A Motion-Direction-Detecting Model for Gray-Scale Images Based on the Hassenstein–Reichardt Model |
| title_short | A Motion-Direction-Detecting Model for Gray-Scale Images Based on the Hassenstein–Reichardt Model |
| title_sort | motion direction detecting model for gray scale images based on the hassenstein reichardt model |
| topic | artificial visual system amacrine cells mechanism neuron motion detection gray-scale |
| url | https://www.mdpi.com/2079-9292/12/11/2481 |
| work_keys_str_mv | AT zhiyuqiu amotiondirectiondetectingmodelforgrayscaleimagesbasedonthehassensteinreichardtmodel AT yukitodo amotiondirectiondetectingmodelforgrayscaleimagesbasedonthehassensteinreichardtmodel AT chenyangyan amotiondirectiondetectingmodelforgrayscaleimagesbasedonthehassensteinreichardtmodel AT zhengtang amotiondirectiondetectingmodelforgrayscaleimagesbasedonthehassensteinreichardtmodel AT zhiyuqiu motiondirectiondetectingmodelforgrayscaleimagesbasedonthehassensteinreichardtmodel AT yukitodo motiondirectiondetectingmodelforgrayscaleimagesbasedonthehassensteinreichardtmodel AT chenyangyan motiondirectiondetectingmodelforgrayscaleimagesbasedonthehassensteinreichardtmodel AT zhengtang motiondirectiondetectingmodelforgrayscaleimagesbasedonthehassensteinreichardtmodel |