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

scholarly journals An Application of Computer Vision Systems to Solve the Problem of Unmanned Aerial Vehicle Control

2014 ◽  
Vol 15 (3) ◽  
pp. 209-214 ◽  
Author(s):  
Alexey Y. Aksenov ◽  
Sergey V. Kuleshov ◽  
Alexandra A. Zaytseva
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Unmanned Aerial Vehicle ◽  
Vehicle Control ◽  
Vision Systems ◽  
Absolute Positioning ◽  
Automatic Landing ◽  
Aerial Vehicle ◽  
Computer Vision Systems ◽  
Aerial Platform

Abstract The paper considers an approach for application of computer vision systems to solve the problem of unmanned aerial vehicle control. The processing of images obtained through onboard camera is required for absolute positioning of aerial platform (automatic landing and take-off, hovering etc.) used image processing on-board camera. The proposed method combines the advantages of existing systems and gives the ability to perform hovering over a given point, the exact take-off and landing. The limitations of implemented methods are determined and the algorithm is proposed to combine them in order to improve the efficiency.

scholarly journals Image Processing of Motion for Security Applications

2017 ◽  
Vol 13 (27) ◽  
pp. 44 ◽  
Author(s):  
Frantisek Duchon ◽  
Peter Bučka ◽  
Martina Szabová ◽  
Martin Dekan ◽  
Peter Beňo ◽  
...  
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Control System ◽  
Digital Video ◽  
Moving Object ◽  
Vision Systems ◽  
Minimal Level ◽  
Camera System ◽  
Security Applications ◽  
Computer Vision Systems

The aim of the article is a design, execution and examination of the computer vision systems, which processes digital video, reduces noise to a minimal level, and identifies a moving object together with estimation of its distance from the camera. For the image processing, library OpenCV was used. Two different methods were examined and implemented in control system. Some results are very similar in character and functionality with the use of security camera system, but the determining the distance of a given object is a new advanced ability of proposed system.

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.

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