Video | EPFL Graph Search

Video is an electronic medium for the recording, copying, playback, broadcasting, and display of moving media. Video was first developed for mechanical television systems, which were quickly replaced by cathode-ray tube (CRT) systems which, in turn, were replaced by flat panel displays of several types. Video systems vary in display resolution, aspect ratio, refresh rate, color capabilities and other qualities. Analog and digital variants exist and can be carried on a variety of media, including radio broadcasts, magnetic tape, optical discs, , and network streaming. Etymology The word video comes from the Latin videre, meaning "I see" or "to see", with the added suffix -eo denoting to audio. Thus, it literally means "seeing audio". History History of television Analog video Analog television and Videotape Video was invented decades after film, which records a sequence of miniature images visible to the eye when the film is physically examined. Vi

Maintainable Online Classes

Alexandre David Koo

This project was initialized by Professor Prandoni from EPFL in the context of a bachelor project entitled "Maintainable Online Coursese". The motivations for this project was the lack of intuitive slideshow annotation software considering the current boom of online classes, most notably the MOOCs. One of the major limitations of current options are the fact that annotations and video tracks are interwoven, giving very little editing power after a recording has been done. Such editing could come in handy to correct mistakes or update a video lecture. In this application, we attempt to seperate out the two worlds of annotations and slide transitions(video) and experiement to see if such a setup could be beneficial to the workflow. In order to achieve this result, the approach has been to carefully and precisely register every slide transition and pen stroke timing, to then be able to succesfully replay the session but with full modularity when it comes to editing and modifications.

2016

Semi-supervised Extraction of Audio-Visual Sources

Patricia Calatayud Martinez

This report presents a semi-supervised method to jointly extract audio-visual sources from a scene. It consist of applying a supervised method to segment the video signal followed by an automatic process to properly separate the audio track. This approach starts with the user interaction to select the audio-visual target we want to cut. This labels the problem into two parts. One is the foreground, the object we aim to extract. The background is the remaining content which sets the other part. The segmentation method will be performed over the video signal to split its visual content into foreground and background. It is based on describing the video information as a volume. It is composed of pixels and relationships among them which set the features of the problem. This 3D structure can be interpreted as a graph. Therefore the segmentation is achieved by applying a Graph Cuts algorithm which provides the suitable results. In this work the audio separation process is an automatic task. Due to this we need to extract prior information from the audio-visual data available. Therefore a link between audio and video channels is deﬁned by an audio-visual motion map. This is a video sequence which provides the amount of synchronous motion with the audio track. An audio-based video diﬀusion method have been developed to obtain this kind of signal. Applying the video segmentation labelling to the audio-visual motion map we obtain the distribution of the amount motion into foreground and background. According to the range of these values it is possible to asses when the corresponding audio is on/oﬀ. At this point, audio samples of sources can be extract when the audio activity is due to only one. This is the prior information to learn audio models by means of spectral GMM estimation to assess the audio signals. Finally the separation method is applied. It consist of looking for the more suitable couple of states given the mixture spectrum of the total audio track. Therefore we achieve the audio separation goal. In this work we will study in detail all the stages towards the ﬁnal aim of our method, analyzing the performances of our approach.

2010

AI-based telepresence for broadcast applications

Rayan Daod Nathoo, Touradj Ebrahimi

In this paper, we introduce a new solution and underlying architecture that allows remote participants to interact with hosts in a broadcast scenario. To achieve this, background extraction is first applied to video received from remote participants to extract their faces and bodies. Considering that the video from remote participants are usually of lower resolutions when compared to content produced by professional cameras in production, we propose to scale the extracted video with a super-resolution module. Finally, the processed video from remote participants are merged with studio video and streamed to audiences. Given the real-time and high-quality requirements, both background extraction and super-resolution modules are learning-based solutions and run on GPUs. The proposed solution has been deployed in the Advance Mixed Reality (AdMiRe) project. The objective and subjective assessment results show that the proposed solution works well in real world applications.

SPIE2022

Semi-supervised Extraction of Audio-Visual Sources

Patricia Calatayud Martinez

2010