Crosswind estimations by using correlation and spectral video processing algorithms

2017 ◽  
Author(s):  
Alexey L. Afanasiev ◽  
Viktor A. Banakh ◽  
Dmitry A. Marakasov
Keyword(s):  
Video Processing ◽  
Processing Algorithms

scholarly journals Technical review in digital image/video processing algorithms

2018 ◽  
Vol 2018 (1) ◽  
pp. 34-39
Author(s):  
Daniel Rodrigues Ferraz Izario ◽  
Yuzo Iano ◽  
Bruno Rodrigues Ferraz Izario ◽  
Diego Arturo Pajuelo Castro
Keyword(s):  
Digital Image ◽  
Video Processing ◽  
Technical Review ◽  
Processing Algorithms

scholarly journals Audio, Image, Video, and Weather Datasets for Continuous Electronic Beehive Monitoring

2021 ◽  
Vol 11 (10) ◽  
pp. 4632
Author(s):  
Vladimir Kulyukin
Keyword(s):  
Video Processing ◽  
Data Science ◽  
State Of The Art ◽  
Image And Video Processing ◽  
Performance Benchmarks ◽  
Northern Utah ◽  
Stable Performance ◽  
Processing Algorithms ◽  
Forager Traffic ◽  
Point Of Reference

In 2014, we designed and implemented BeePi, a multi-sensor electronic beehive monitoring system. Since then we have been using BeePi monitors deployed at different apiaries in northern Utah to design audio, image, and video processing algorithms to analyze forager traffic in the vicinity of Langstroth beehives. Since our first publication on BeePi in 2016, we have received multiple requests from researchers and practitioners for the datasets we have used in our research. The main objective of this article is to provide a comprehensive point of reference to the datasets that we have so far curated for our research. We hope that our datasets will provide stable performance benchmarks for continuous electronic beehive monitoring, help interested parties verify our findings and correct errors, and advance the state of the art in continuous electronic beehive monitoring and related areas of AI, machine learning, and data science.

scholarly journals AUVANA: An Automated Video Analysis Tool for Visual Complexity

2021 ◽  
Author(s):  
Emad Alghamdi ◽  
Eduardo Velloso ◽  
Paul Gruba
Keyword(s):  
Video Analysis ◽  
Video Processing ◽  
Visual Complexity ◽  
Important Variable ◽  
Analysis Tool ◽  
Digital Videos ◽  
Potential Applications ◽  
Processing Algorithms ◽  
Automated Video Analysis ◽  
The Impact

Visual complexity is widely considered to be an important variable underlying visual perception. While videos have become versatile in their use of visual imagery, surprisingly, little research has been devoted to understanding the impact of visual complexity. In this paper, we present Automated Video Analysis (AUVANA) software, an open-source tool for extracting, computing, and visualizing visual complexity in digital videos. Through leveraging more sophisticated computer vision and video processing algorithms, AUVANA automatically extracts and computes 78 video visual complexity indices. Results of explanatory analyses demonstrated that rather than a unitary construct video visual complexity is more likely a multidimensional and multifaceted phenomenon. We conclude the paper with a discussion about the potential applications of the software

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