Fast Arc Detection Algorithm for Play Field Registration in Soccer Video Mining

2006 ◽  
Author(s):  
Fei Wang ◽  
Lifeng Sun ◽  
Bo Yang ◽  
Shiqiang Yang
Keyword(s):  
Detection Algorithm ◽  
Video Mining ◽  
Soccer Video

A Robust Replay Detection Algorithm for Soccer Video

2011 ◽  
Vol 18 (9) ◽  
pp. 509-512 ◽  
Author(s):  
Wei Xu ◽  
Yang Yi
Keyword(s):  
Detection Algorithm ◽  
Soccer Video

scholarly journals Estimation of Wind Turbine Angular Velocity Remotely Found on Video Mining and Convolutional Neural Network

2020 ◽  
Vol 10 (10) ◽  
pp. 3544 ◽  
Author(s):  
Mahdi Bahaghighat ◽  
Qin Xin ◽  
Seyed Ahmad Motamedi ◽  
Morteza Mohammadi Zanjireh ◽  
Antoine Vacavant
Keyword(s):  
Deep Learning ◽  
Angular Velocity ◽  
Wind Turbine ◽  
Intelligent Systems ◽  
Wind Farm ◽  
Vision System ◽  
Detection Algorithm ◽  
Single Shot ◽  
Video Mining ◽  
Other Information

Today, energy issues are more important than ever. Because of the importance of environmental concerns, clean and renewable energies such as wind power have been most welcomed globally, especially in developing countries. Worldwide development of these technologies leads to the use of intelligent systems for monitoring and maintenance purposes. Besides, deep learning as a new area of machine learning is sharply developing. Its strong performance in computer vision problems has conducted us to provide a high accuracy intelligent machine vision system based on deep learning to estimate the wind turbine angular velocity, remotely. This velocity along with other information such as pitch angle and yaw angle can be used to estimate the wind farm energy production. For this purpose, we have used SSD (Single Shot Multi-Box Detector) object detection algorithm and some specific classification methods based on DenseNet, SqueezeNet, ResNet50, and InceptionV3 models. The results indicate that the proposed system can estimate rotational speed with about 99.05 % accuracy.

Read, Understand, Learn, & Excel: Development and Testing of an Automated Reading Strategy Detection Algorithm for Postsecondary Students

2019 ◽  
Vol 28 (3) ◽  
pp. 1257-1267 ◽  
Author(s):  
Priya Kucheria ◽  
McKay Moore Sohlberg ◽  
Jason Prideaux ◽  
Stephen Fickas
Keyword(s):  
Academic Success ◽  
Undergraduate Students ◽  
Expository Text ◽  
Reading Strategy ◽  
Detection Algorithm ◽  
Expository Texts ◽  
Human Observer ◽  
Behavioral Strategies ◽  
Periodic Review ◽  
Wide Range

PurposeAn important predictor of postsecondary academic success is an individual's reading comprehension skills. Postsecondary readers apply a wide range of behavioral strategies to process text for learning purposes. Currently, no tools exist to detect a reader's use of strategies. The primary aim of this study was to develop Read, Understand, Learn, & Excel, an automated tool designed to detect reading strategy use and explore its accuracy in detecting strategies when students read digital, expository text.MethodAn iterative design was used to develop the computer algorithm for detecting 9 reading strategies. Twelve undergraduate students read 2 expository texts that were equated for length and complexity. A human observer documented the strategies employed by each reader, whereas the computer used digital sequences to detect the same strategies. Data were then coded and analyzed to determine agreement between the 2 sources of strategy detection (i.e., the computer and the observer).ResultsAgreement between the computer- and human-coded strategies was 75% or higher for 6 out of the 9 strategies. Only 3 out of the 9 strategies–previewing content, evaluating amount of remaining text, and periodic review and/or iterative summarizing–had less than 60% agreement.ConclusionRead, Understand, Learn, & Excel provides proof of concept that a reader's approach to engaging with academic text can be objectively and automatically captured. Clinical implications and suggestions to improve the sensitivity of the code are discussed.Supplemental Materialhttps://doi.org/10.23641/asha.8204786

Low-Complexity Soft-ML Detection Algorithm for Modified-DCM in WiMedia UWB Systems

2013 ◽  
Vol E96.B (3) ◽  
pp. 910-913 ◽  
Author(s):  
Kilhwan KIM ◽  
Jangyong PARK ◽  
Jihun KOO ◽  
Yongsuk KIM ◽  
Jaeseok KIM
Keyword(s):  
Low Complexity ◽  
Detection Algorithm ◽  
Ml Detection

Closed Summation Expressions for PD and PFA of Adaptive Sidelobe Blanker Detection Algorithm

2012 ◽  
Vol E95-B (2) ◽  
pp. 676-679 ◽  
Author(s):  
Guolong CUI ◽  
Lingjiang KONG ◽  
Xiaobo YANG ◽  
Jianyu YANG
Keyword(s):  
Detection Algorithm
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