Disk scheduling in video editing systems

2001 ◽  
Vol 13 (6) ◽  
pp. 933-950 ◽  
Author(s):  
W.G. Aref ◽  
I. Kamel ◽  
S. Ghandeharizadeh
Keyword(s):  
Video Editing ◽  
Disk Scheduling

An Hybridized Disk Scheduling Algorithm (HDSA)

2019 ◽  
Vol 9 (3) ◽  
pp. p8789
Author(s):  
Akanmu T.A. ◽  
Aadegoke B.O. ◽  
Oladoye S.F.
Keyword(s):  
Scheduling Algorithm ◽  
Disk Scheduling

scholarly journals 5 Editing Research: Video Editing

2021 ◽  
pp. 72-81
Author(s):  
Léa Klaue ◽  
Eric Andreae
Keyword(s):  
Video Editing

scholarly journals Copy-Move Forgery Detection (CMFD) Using Deep Learning for Image and Video Forensics

2021 ◽  
Vol 7 (3) ◽  
pp. 59
Author(s):  
Yohanna Rodriguez-Ortega ◽  
Dora M. Ballesteros ◽  
Diego Renza
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Training Data ◽  
Video Editing ◽  
Forgery Detection ◽  
Copy Move Forgery Detection ◽  
The Impact ◽  
And Training ◽  
Selection Of ◽  
Traditional Image

With the exponential growth of high-quality fake images in social networks and media, it is necessary to develop recognition algorithms for this type of content. One of the most common types of image and video editing consists of duplicating areas of the image, known as the copy-move technique. Traditional image processing approaches manually look for patterns related to the duplicated content, limiting their use in mass data classification. In contrast, approaches based on deep learning have shown better performance and promising results, but they present generalization problems with a high dependence on training data and the need for appropriate selection of hyperparameters. To overcome this, we propose two approaches that use deep learning, a model by a custom architecture and a model by transfer learning. In each case, the impact of the depth of the network is analyzed in terms of precision (P), recall (R) and F1 score. Additionally, the problem of generalization is addressed with images from eight different open access datasets. Finally, the models are compared in terms of evaluation metrics, and training and inference times. The model by transfer learning of VGG-16 achieves metrics about 10% higher than the model by a custom architecture, however, it requires approximately twice as much inference time as the latter.

A Parent-Implemented Shared Reading Intervention via Telepractice

2021 ◽  
pp. 105381512110322
Author(s):  
Yusuf Akemoglu ◽  
Vanessa Hinton ◽  
Dayna Laroue ◽  
Vanessa Jefferson
Keyword(s):  
Time Delay ◽  
Single Case ◽  
Multiple Baseline ◽  
Video Editing ◽  
Baseline Design ◽  
Communicative Acts ◽  
Use Of Time ◽  
Young Children With Disabilities ◽  
Evidenced Based ◽  
Feedback Delivery

We describe a study of the internet-based Parent-Implemented Communication Strategies–Storybook (i-PiCSS), an intervention designed to train and coach parents to use evidenced-based naturalistic communication teaching (NCT) strategies (i.e., modeling, mand-model, and time delay) and RTs while reading storybooks with their young children with disabilities. Three participating parents were trained and coached via telepractice technologies (videoconferences, video editing software). Zoom software was used for videoconferencing and Camtasia software was used to record the training and coaching sessions and to edit the recorded session for feedback delivery purposes. Using a single-case multiple-baseline design across NCT strategies within each family, we examined (a) parents’ fidelity use of the three NCT strategies, (b) parents’ use of book RTs, and (c) child language and communication outcomes. The entire intervention period lasted 8 weeks. After training and coaching, parents used the modeling, mand-model, and time delay strategies with higher rates and higher quality (accuracy). Children initiated more communicative acts upon parents’ use of time delay and increased their numbers of single- and multiple-word responses.

scholarly journals Coview: A Cooperative Architecture for Digital Video Editing

2006 ◽  
Vol 115 (11-12) ◽  
pp. 482-491 ◽  
Author(s):  
Benjamim Fonseca ◽  
Eurico Carrapatoso
Keyword(s):  
Digital Video ◽  
Video Editing

scholarly journals MovieRemix: Having Fun Playing with Videos

2011 ◽  
Vol 2011 ◽  
pp. 1-13 ◽  
Author(s):  
Nicola Dusi ◽  
Maria Federico ◽  
Marco Furini
Keyword(s):  
Simple Game ◽  
Source Material ◽  
Video Editing ◽  
Technological Aspect ◽  
Server Side ◽  
Challenging Environment ◽  
Streaming Videos ◽  
And Storage ◽  
Client Device ◽  
Different Levels

The process of producing new creative videos by editing, combining, and organizing pre-existing material (e.g., video shots) is a popular phenomenon in the current web scenario. Known asremixor video remix, the produced video may have new and different meanings with respect to the source material. Unfortunately, when managing audiovisual objects, the technological aspect can be a burden for many creative users. Motivated by the large success of the gaming market, we propose a novel game and an architecture to make the remix process a pleasant and stimulating gaming experience. MovieRemix allows people to act like a movie director, but instead of dealing with cast and cameras, the player has to create a remixed video starting from a given screenplay and from video shots retrieved from the provided catalog. MovieRemix is not a simple video editing tool nor is a simple game: it is a challenging environment that stimulates creativity. To temp to play the game, players can access different levels of screenplay (original, outline, derived) and can also challenge other players. Computational and storage issues are kept at the server side, whereas the client device just needs to have the capability of playing streaming videos.

A new real time disk-scheduling method based on GSR algorithm

2010 ◽  
Vol 83 (11) ◽  
pp. 2147-2164
Author(s):  
Hossein Rahmani ◽  
Mohammad Mehdi Faghih ◽  
Mohsen Ebrahimi Moghaddam
Keyword(s):  
Real Time ◽  
Disk Scheduling ◽  
Scheduling Method

Scalable multimedia disk scheduling

2004 ◽  
Author(s):  
M.F. Mokbel ◽  
W.G. Aref ◽  
K. Elbassioni ◽  
I. Kamel
Keyword(s):  
Disk Scheduling
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