Statistical Characteristics of Optical Signals and Images in Machine Vision Systems

2021 ◽  
pp. 134-162
Author(s):  
Tatyana A. Strelkova ◽  
Alexander P. Lytyuga ◽  
Alexander S. Kalmykov
Keyword(s):  
Decision Making ◽  
Machine Vision ◽  
Statistical Properties ◽  
Statistical Characteristics ◽  
Stochastic Flow ◽  
Vision Systems ◽  
Optical Signals ◽  
Input And Output ◽  
Statistical Detection ◽  
Flow Theories

The chapter is devoted to the creation of a comprehensive approach to the physical and mathematical description of signals in optoelectronics in machine vision, taking into account the phenomena of interaction of optical radiation with system elements. A new methodology for the study of the statistical properties of input and output signals in optoelectronic systems is proposed, taking into account the availability of grouped statistical properties that do not obey the Poisson statistics. The basis is the joint use of wave and corpuscular description of signals in systems, stochastic flow theories, and elements of statistical detection theory. Information and energetic technology have been developed that integrates the theoretical justification of signal description under various observation conditions and decision-making methods.

Intuition Machines

2020 ◽  
Vol 9 (1) ◽  
pp. 54-68
Author(s):  
Linda Kronman
Keyword(s):  
Decision Making ◽  
Machine Vision ◽  
Environmental Security ◽  
First Century ◽  
Close Reading ◽  
Vision Systems ◽  
Political Crises ◽  
Early Twenty ◽  
The World ◽  
Human Decision

The urgency of environmental, security, economic and political crises in the early twenty-first century has propelled the use of machine vision to aid human decision-making. These developments have led to strategies in which functions of human intuitive processing have been externalized to ‘vision machines’ in the hope of optimized and objective insights. I argue that we should approach these replacements of human nonconscious functions as ‘intuition machines.’ I apply this approach through a close reading of artworks which expose the hid- den labour required to train a machine. These artworks demonstrate how human agency shapes the ways that machines perceive the world and reveal how values and biases are hardcoded into nonconscious cognitive machine vision systems. Thus, my analysis suggests that decisions made by such systems cannot be considered fundamentally objective or true. Nevertheless, artworks also exemplify how externalized intuitive processing can still be helpful as long as we refrain from blindly taking the results as a go-signal to take immediate action.

scholarly journals Machine Vision Systems for Plant Growth Measurement and Modeling

2006 ◽  
Vol 44 (3) ◽  
pp. 181-187 ◽  
Author(s):  
Ta-Te LIN ◽  
Chung-Fang CHIEN ◽  
Wen-Chi LIAO ◽  
Kuo-Chi CHUNG ◽  
Jen-Min CHANG
Keyword(s):  
Plant Growth ◽  
Machine Vision ◽  
Vision Systems ◽  
Growth Measurement ◽  
Plant Growth Measurement

scholarly journals Cyborg Groups Enhance Face Recognition in Crowded Environments

2018 ◽  
Author(s):  
Davide Valeriani ◽  
Riccardo Poli
Keyword(s):  
Decision Making ◽  
Face Recognition ◽  
Machine Vision ◽  
Decision Confidence ◽  
Computer Interfaces ◽  
Recognition Experiment ◽  
Or Groups ◽  
Individual Decisions ◽  
Human Participants ◽  
Crowded Environments

AbstractRecognizing a person in a crowded environment is a challenging, yet critical, visual-search task for both humans and machine-vision algorithms. This paper explores the possibility of combining a residual neural network (ResNet), brain-computer interfaces (BCIs) and human participants to create “cyborgs” that improve decision making. Human participants and a ResNet undertook the same face-recognition experiment. BCIs were used to decode the decision confidence of humans from their EEG signals. Different types of cyborg groups were created, including either only humans (with or without the BCI) or groups of humans and the ResNet. Cyborg groups decisions were obtained weighing individual decisions by confidence estimates. Results show that groups of cyborgs are significantly more accurate (up to 35%) than the ResNet, the average participant, and equally-sized groups of humans not assisted by technology. These results suggest that melding humans, BCI, and machine-vision technology could significantly improve decision-making in realistic scenarios.

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