The Mechanism of Orientation Detection Based on Artificial Visual System for Greyscale Images

Xiliang Zhang, Sichen Tao, Zheng Tang, Shuxin Zheng () and Yoki Todo ()
Additional contact information
Xiliang Zhang: Faculty of Engineering, University of Toyama, Toyama-shi 930-8555, Japan
Sichen Tao: Faculty of Engineering, University of Toyama, Toyama-shi 930-8555, Japan
Zheng Tang: Faculty of Engineering, University of Toyama, Toyama-shi 930-8555, Japan
Shuxin Zheng: School of Economics and Business, Changzhou Vocational Institute of Textile and Garment, Changzhou 213164, China
Yoki Todo: Faculty of Electrical and Computer Engineering, Kanazawa University, Kanazawa-shi 920-1192, Japan

Mathematics, 2023, vol. 11, issue 12, 1-13

Abstract: 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.

Keywords: artificial visual system; orientation detection; dendritic neuron model; convolutional neural network; noise resistance Greyscale Images (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/11/12/2715/pdf (application/pdf)
https://www.mdpi.com/2227-7390/11/12/2715/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:11:y:2023:i:12:p:2715-:d:1171886

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().