Video Processing in an FPGA-Enabled Ethernet Switch

SMPTE Motion Imaging Journal ◽

10.5594/j18384 ◽

2014 ◽

Vol 123 (2) ◽

pp. 48-52 ◽

Cited By ~ 1

Author(s):

Thomas Edwards ◽

Warren Belkin ◽

Andy Bechtolsheim

Keyword(s):

Video Processing ◽

Ethernet Switch

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Modern Technology for Image processing and Computer vision -A Review

Journal of Advanced Sciences and Engineering Technologies ◽

10.32441/jaset.v1i2.178 ◽

2018 ◽

Vol 1 (2) ◽

pp. 17-23

Author(s):

Takialddin Al Smadi

Keyword(s):

Image Processing ◽

Computer Vision ◽

Augmented Reality ◽

Video Compression ◽

Video Processing ◽

Vision System ◽

Citrus Fruit ◽

Modern Technology ◽

Image And Video Processing ◽

Depth Video

This survey outlines the use of computer vision in Image and video processing in multidisciplinary applications; either in academia or industry, which are active in this field.The scope of this paper covers the theoretical and practical aspects in image and video processing in addition of computer vision, from essential research to evolution of application.In this paper a various subjects of image processing and computer vision will be demonstrated ,these subjects are spanned from the evolution of mobile augmented reality (MAR) applications, to augmented reality under 3D modeling and real time depth imaging, video processing algorithms will be discussed to get higher depth video compression, beside that in the field of mobile platform an automatic computer vision system for citrus fruit has been implemented ,where the Bayesian classification with Boundary Growing to detect the text in the video scene. Also the paper illustrates the usability of the handed interactive method to the portable projector based on augmented reality. © 2018 JASET, International Scholars and Researchers Association

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Classification of Esophageal and Bolus Movements for Evaluation of Swallowing Function by Ultrasound Video Processing Using Optical Flow and Stable Extremal Region Method

IEEJ Transactions on Electronics Information and Systems ◽

10.1541/ieejeiss.139.603 ◽

2019 ◽

Vol 139 (5) ◽

pp. 603-608 ◽

Cited By ~ 2

Author(s):

Yutaka Suzuki ◽

Kyosuke Hatsushika ◽

Keisuke Masuyama ◽

Osamu Sakata ◽

Morimasa Tanimoto ◽

...

Keyword(s):

Optical Flow ◽

Video Processing ◽

Swallowing Function ◽

Region Method

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VIDEO PROCESSING IN VISUAL SYSTEM FOR COLOR DETECTION FOR PEOPLE WITH TRITANOPIA

i-manager s Journal on Information Technology ◽

10.26634/jit.7.3.14517 ◽

2018 ◽

Vol 7 (3) ◽

pp. 17

Author(s):

A. PENDHARI NAZNEEN ◽

R. SEDAMKAR RAGHAVENDRA ◽

◽

Keyword(s):

Visual System ◽

Video Processing ◽

Color Detection

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Learning Algorithms for Audio and Video Processing: Independent Component Analysis and Support Vector Machine Based Approaches

10.21236/ada458739 ◽

2000 ◽

Author(s):

Yuan Qi

Keyword(s):

Support Vector Machine ◽

Independent Component Analysis ◽

Video Processing ◽

Learning Algorithms ◽

Component Analysis ◽

Independent Component ◽

Support Vector

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Smart Care Using a DNN-Based Approach for Activities of Daily Living (ADL) Recognition

Applied Sciences ◽

10.3390/app11010010 ◽

2020 ◽

Vol 11 (1) ◽

pp. 10

Author(s):

Muchun Su ◽

Diana Wahyu Hayati ◽

Shaowu Tseng ◽

Jiehhaur Chen ◽

Hsihsien Wei

Keyword(s):

Neural Network ◽

Health Care ◽

Activities Of Daily Living ◽

Video Processing ◽

Daily Living ◽

Deep Neural Network ◽

Recognition System ◽

Automatic Recognition ◽

Data Filtering ◽

Accuracy Rate

Health care for independently living elders is more important than ever. Automatic recognition of their Activities of Daily Living (ADL) is the first step to solving the health care issues faced by seniors in an efficient way. The paper describes a Deep Neural Network (DNN)-based recognition system aimed at facilitating smart care, which combines ADL recognition, image/video processing, movement calculation, and DNN. An algorithm is developed for processing skeletal data, filtering noise, and pattern recognition for identification of the 10 most common ADL including standing, bending, squatting, sitting, eating, hand holding, hand raising, sitting plus drinking, standing plus drinking, and falling. The evaluation results show that this DNN-based system is suitable method for dealing with ADL recognition with an accuracy rate of over 95%. The findings support the feasibility of this system that is efficient enough for both practical and academic applications.

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