Optically Modulated HfS2-Based Synapses for Artificial Vision Systems

2021 ◽  
Author(s):  
Hao Xiong ◽  
Liping Xu ◽  
Caifang Gao ◽  
Qing Zhang ◽  
Menghan Deng ◽  
...  
Keyword(s):  
Artificial Vision ◽  
Vision Systems

Deep Learning: The Good, the Bad, and the Ugly

2019 ◽  
Vol 5 (1) ◽  
pp. 399-426 ◽  
Author(s):  
Thomas Serre
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Machine Vision ◽  
Artificial Vision ◽  
Vision Systems ◽  
Comprehensive Overview ◽  
Biological Vision ◽  
Recent Developments ◽  
Visual Intelligence ◽  
Modern Machine

Artificial vision has often been described as one of the key remaining challenges to be solved before machines can act intelligently. Recent developments in a branch of machine learning known as deep learning have catalyzed impressive gains in machine vision—giving a sense that the problem of vision is getting closer to being solved. The goal of this review is to provide a comprehensive overview of recent deep learning developments and to critically assess actual progress toward achieving human-level visual intelligence. I discuss the implications of the successes and limitations of modern machine vision algorithms for biological vision and the prospect for neuroscience to inform the design of future artificial vision systems.

Correlation between Roughness (Ra) and Fractal Dimension (D) Using Artificial Vision Systems for On-Site Inspection

2018 ◽  
Vol 22 (4) ◽  
Author(s):  
José Gabriel Ayala Landeros ◽  
Victor Manuel Castaño Meneses ◽  
María Blanca Becerra Rodríguez ◽  
Saulo Servín Guzmán ◽  
Sonia Elizabeth Román Flores ◽  
...  
Keyword(s):  
Fractal Dimension ◽  
Artificial Vision ◽  
Vision Systems ◽  
On Site Inspection

scholarly journals Guarding Against Adversarial Attacks using Biologically Inspired Contour Integration

2018 ◽  
Vol 4 (1) ◽  
pp. 3
Author(s):  
Salman Khan ◽  
Alexander Wong ◽  
Bryan Tripp
Keyword(s):  
Contour Integration ◽  
Artificial Vision ◽  
Vision Systems ◽  
High Confidence ◽  
Biologically Inspired ◽  
The Brain

Artificial vision systems are susceptible to adversarial attacks. Smallintentional changes to images can cause these systems to mis-classify with high confidence. The brain has many mechanisms forstrengthening weak or confusing inputs. One such technique, con-tour integration can separate objects from irrelevant background.We show that incorporating contour integration within artificial vi-sual systems can increase their robustness to adversarial attacks.

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