Real-Time Sensing, Reasoning and Adaptation for Computer Vision Systems

2021 ◽  
pp. 573-585
Author(s):  
Volodymyr Hrytsyk ◽  
Mariia Nazarkevych
Keyword(s):  
Computer Vision ◽  
Real Time ◽  
Vision Systems ◽  
Computer Vision Systems

scholarly journals Image processing in real-time computer vision systems using FPGA

2016 ◽  
pp. 1-16
Author(s):  
Alexander Sergeevich Derzhanovsky ◽  
Sergey Mikhailovich Sokolov
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Real Time ◽  
Vision Systems ◽  
Computer Vision Systems

A Survey of Techniques for Real Time Computer Vision Systems

2015 ◽  
Vol 4 (2) ◽  
pp. 24-35
Author(s):  
E. Sabarinathan ◽  
◽  
E. Manoj ◽  
Keyword(s):  
Computer Vision ◽  
Real Time ◽  
Vision Systems ◽  
Computer Vision Systems

scholarly journals 383 Harnessing the Power of Computer Vision System to Improve Management Decisions in Livestock Operations

2020 ◽  
Vol 98 (Supplement_4) ◽  
pp. 138-139
Author(s):  
Joao R Dorea ◽  
Tiago Bresolin ◽  
Rafael E P Ferreira ◽  
Luiz Gustavo R Pereira
Keyword(s):  
Computer Vision ◽  
Body Weight ◽  
Real Time ◽  
Vision System ◽  
Labor Costs ◽  
Computer Vision System ◽  
Vision Systems ◽  
Management Decisions ◽  
Level Information ◽  
Computer Vision Systems

Abstract In livestock operations, systematically monitoring animal body weight, biometric body measurements, animal behavior, feed bunk, and other complex phenotypes is unfeasible due to labor, costs, and animal stress. Applications of computer vision are growing in importance in livestock systems due to their ability to generate real-time, non-invasive, and accurate animal-level information. Such technology has emerged as a powerful tool to predict animal identification, body weight, biometric measurements, complex behavioral traits, and feed bunk score. However, the development of a computer vision system requires sophisticated statistical and computational approaches for efficient data management and appropriate data mining, as it involves massive datasets. The objective of this talk is to provide an overview of how computer vision systems can be an effective tool to integrate animal-level information and to create predictive modeling for precise management decisions. We will discuss some of the challenges, applications, and potentials of computer vision systems in livestock, and some examples to be presented include: (1) monitoring animal growth and behavior; (2) automated feed bunk management; (3) individual animal recognition; and (4) particle size distribution in total mixed ration. The development of computer vision technologies will potentially have a major impact in the livestock industry by predicting real-time and accurate phenotypes, which, in the future, could be used to improve farm management decisions, breeding programs, and to build optimal data-driven interventions.

The machine training in problems of satellite images’s processing

Metrologiya ◽  
2020 ◽  
pp. 15-37
Author(s):  
L. P. Bass ◽  
Yu. A. Plastinin ◽  
I. Yu. Skryabysheva
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Computer Vision ◽  
Satellite Images ◽  
Vision Systems ◽  
Earth Remote Sensing ◽  
Practical Applications ◽  
Convolution Neural Networks ◽  
Computer Vision Systems ◽  
Trained Neural Network

Use of the technical (computer) vision systems for Earth remote sensing is considered. An overview of software and hardware used in computer vision systems for processing satellite images is submitted. Algorithmic methods of the data processing with use of the trained neural network are described. Examples of the algorithmic processing of satellite images by means of artificial convolution neural networks are given. Ways of accuracy increase of satellite images recognition are defined. Practical applications of convolution neural networks onboard microsatellites for Earth remote sensing are presented.

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