Introduction to Video Processing

2010 ◽  
pp. 223-231 ◽  
Author(s):  
Michael Parker
Keyword(s):  
Video Processing

Modern Technology for Image processing and Computer vision -A Review

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

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

2018 ◽  
Vol 7 (3) ◽  
pp. 17
Author(s):  
A. PENDHARI NAZNEEN ◽  
R. SEDAMKAR RAGHAVENDRA ◽  
◽  
Keyword(s):  
Visual System ◽  
Video Processing ◽  
Color Detection

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

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.

Design of a real-time video processing system with FPGA

2002 ◽  
Author(s):  
Wei Liu ◽  
Zeying Chi ◽  
Wenjian Chen
Keyword(s):  
Real Time ◽  
Video Processing ◽  
Processing System

scholarly journals Image and video processing on mobile devices: a survey

2021 ◽  
Author(s):  
Chamin Morikawa ◽  
Michihiro Kobayashi ◽  
Masaki Satoh ◽  
Yasuhiro Kuroda ◽  
Teppei Inomata ◽  
...  
Keyword(s):  
Mobile Devices ◽  
Video Processing ◽  
Image And Video Processing

scholarly journals GPU-Enabled Serverless Workflows for Efficient Multimedia Processing

2021 ◽  
Vol 11 (4) ◽  
pp. 1438
Author(s):  
Sebastián Risco ◽  
Germán Moltó
Keyword(s):  
Video Processing ◽  
Cost Effective ◽  
Multimedia Processing ◽  
Computing Services ◽  
Video Frames ◽  
Open Source Framework ◽  
Cloud Infrastructures ◽  
Aws Lambda ◽  
Runtime Environments

Serverless computing has introduced scalable event-driven processing in Cloud infrastructures. However, it is not trivial for multimedia processing to benefit from the elastic capabilities featured by serverless applications. To this aim, this paper introduces the evolution of a framework to support the execution of customized runtime environments in AWS Lambda in order to accommodate workloads that do not satisfy its strict computational requirements: increased execution times and the ability to use GPU-based resources. This has been achieved through the integration of AWS Batch, a managed service to deploy virtual elastic clusters for the execution of containerized jobs. In addition, a Functions Definition Language (FDL) is introduced for the description of data-driven workflows of functions. These workflows can simultaneously leverage both AWS Lambda for the highly-scalable execution of short jobs and AWS Batch, for the execution of compute-intensive jobs that can profit from GPU-based computing. To assess the developed open-source framework, we executed a case study for efficient serverless video processing. The workflow automatically generates subtitles based on the audio and applies GPU-based object recognition to the video frames, thus simultaneously harnessing different computing services. This allows for the creation of cost-effective highly-parallel scale-to-zero serverless workflows in AWS.

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