Computer vision learning techniques for sports video analytics: removing overlays

2020 ◽  
Author(s):  
Arthur C. Depoian ◽  
Lorenzo E. Jaques ◽  
Dong Xie ◽  
Colleen P. Bailey ◽  
Parthasarathy Guturu
Keyword(s):  
Computer Vision ◽  
Video Analytics ◽  
Sports Video ◽  
Learning Techniques

scholarly journals DeepOSN: Bringing deep learning as malicious detection scheme in online social network

2020 ◽  
Vol 9 (1) ◽  
pp. 146
Author(s):  
Putra Wanda ◽  
Marselina Endah Hiswati ◽  
Huang J. Jie
Keyword(s):  
Social Networks ◽  
Computer Vision ◽  
Deep Learning ◽  
Online Social Networks ◽  
Link Prediction ◽  
Online Social Network ◽  
Detection Scheme ◽  
Malicious Activity ◽  
Manual Analysis ◽  
Learning Techniques

Manual analysis for malicious prediction in Online Social Networks (OSN) is time-consuming and costly. With growing users within the environment, it becomes one of the main obstacles. Deep learning is growing algorithm that gains a big success in computer vision problem. Currently, many research communities have proposed deep learning techniques to automate security tasks, including anomalous detection, malicious link prediction, and intrusion detection in OSN. Notably, this article describes how deep learning makes the OSN security technique more intelligent for detecting malicious activity by establishing a classifier model.

Decay Detection in Citrus Fruits Using Hyperspectral Computer Vision

2012 ◽  
pp. 104-123
Author(s):  
Juan Gómez-Sanchis ◽  
Emilio Soria-Olivas ◽  
Delia Lorente-Garrido ◽  
José M. Martínez-Martínez ◽  
Pablo Escandell-Montero ◽  
...  
Keyword(s):  
Computer Vision ◽  
Agricultural Sector ◽  
Vision System ◽  
Penicillium Digitatum ◽  
Machine Learning Techniques ◽  
Economic Losses ◽  
Citrus Fruits ◽  
Penicillium Italicum ◽  
Citrus Industry ◽  
Learning Techniques

The citrus industry is nowadays an important part of the Spanish agricultural sector. One of the main problems present in the citrus industry is decay caused by Penicillium digitatum and Penicillium italicum fungi. Early detection of decay produced by fungi in citrus is especially important for the citrus industry of distribution. This chapter presents a hyperspectral computer vision system and a set of machine learning techniques in order to detect decay caused by Penicillium digitatum and Penicillium italicum fungi that produce more economic losses to the sector. More specifically, the authors employ a hyperspectral system and artificial neural networks. Nowadays, inspection and removal of damaged citrus is done manually by workers using dangerous ultraviolet light. The proposed system constitutes a feasible and implementable solution for the citrus industry; this has been proven by the fact that several machinery enterprises have shown their interest in the implementation and patent of the system.

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