This document discusses unsupervised learning and clustering algorithms. It begins with an introduction to unsupervised learning, including motivations and differences from supervised learning. It then covers mixture density models, maximum likelihood estimation, and the k-means clustering algorithm. It discusses evaluating clustering using criterion functions and similarity measures. Specific topics covered include normal mixture models, EM algorithm, Euclidean distance, and hierarchical clustering.
Visual object category recognition using weakly supervised learning is the research topic. The goal is to recognize objects based on their visual properties despite challenges from variations in appearance, pose, scale, occlusion, etc. A visual recognition system is proposed that uses bag-of-visual-words modeling and SIFT features. Classification is improved by increasing the visual codebook size and addressing scale differences between training and test images. Keypoint configurations providing structural information are also explored to improve localization, though classification results were better using bag-of-words. Future work focuses on improving the visual codebook and combining segmentation, context, and hierarchical models.
word2vec, LDA, and introducing a new hybrid algorithm: lda2vec👋 Christopher Moody
This document summarizes the lda2vec model, which combines aspects of word2vec and LDA. Word2vec learns word embeddings based on local context, while LDA learns document-level topic mixtures. Lda2vec models words based on both their local context and global document topic mixtures to leverage both approaches. It represents documents as mixtures over sparse topic vectors similar to LDA to maintain interpretability. This allows it to predict words based on local context and global document content.
Semantic Segmentation - Fully Convolutional Networks for Semantic Segmentation岳華 杜
This document discusses several semantic segmentation methods using deep learning, including fully convolutional networks (FCNs), U-Net, and SegNet. FCNs were among the first to use convolutional networks for dense, pixel-wise prediction by converting classification networks to fully convolutional form and combining coarse and fine feature maps. U-Net and SegNet are encoder-decoder architectures that extract high-level semantic features from the input image and then generate pixel-wise predictions, with U-Net copying and cropping features and SegNet using pooling indices for upsampling. These methods demonstrate that convolutional networks can effectively perform semantic segmentation through dense prediction.
Background subtraction is a technique used to separate foreground objects from backgrounds in video frames. It works by comparing each frame to a background model and detecting differences which indicate moving foreground objects. Recursive techniques like mixtures of Gaussians model the background pixel values over time using multiple Gaussian distributions, allowing the background model to adapt to changing lighting conditions. Adaptive background/foreground detection uses a background model that evolves over time to distinguish foreground objects from the background in a robust way.
This document discusses various techniques for question answering and relation extraction in natural language processing. It provides an overview of question answering systems and approaches, including examples like START, Ask Jeeves and Siri. It also discusses using search engines for question answering, relation extraction from questions, and common evaluation metrics for question answering systems like accuracy and mean reciprocal rank.
The document discusses recommender systems and describes several techniques used in collaborative filtering recommender systems including k-nearest neighbors (kNN), singular value decomposition (SVD), and similarity weights optimization (SWO). It provides examples of how these techniques work and compares kNN to SWO. The document aims to explain state-of-the-art recommender system methods.
This document discusses motion detection at night. It notes that while motion detection during daylight has been well-researched, nighttime detection has received less attention due to low-light conditions making objects less visible. Vehicle headlights can also affect background subtraction methods by changing the brightness of surrounding areas. The document proposes considering only dense bright regions corresponding to vehicle headlights to detect motion. It will use a video captured with an infrared camera and software-based image processing techniques like filtering, thresholding and background subtraction to detect and classify moving vehicles at night.
This document discusses unsupervised learning and clustering algorithms. It begins with an introduction to unsupervised learning, including motivations and differences from supervised learning. It then covers mixture density models, maximum likelihood estimation, and the k-means clustering algorithm. It discusses evaluating clustering using criterion functions and similarity measures. Specific topics covered include normal mixture models, EM algorithm, Euclidean distance, and hierarchical clustering.
Visual object category recognition using weakly supervised learning is the research topic. The goal is to recognize objects based on their visual properties despite challenges from variations in appearance, pose, scale, occlusion, etc. A visual recognition system is proposed that uses bag-of-visual-words modeling and SIFT features. Classification is improved by increasing the visual codebook size and addressing scale differences between training and test images. Keypoint configurations providing structural information are also explored to improve localization, though classification results were better using bag-of-words. Future work focuses on improving the visual codebook and combining segmentation, context, and hierarchical models.
word2vec, LDA, and introducing a new hybrid algorithm: lda2vec👋 Christopher Moody
This document summarizes the lda2vec model, which combines aspects of word2vec and LDA. Word2vec learns word embeddings based on local context, while LDA learns document-level topic mixtures. Lda2vec models words based on both their local context and global document topic mixtures to leverage both approaches. It represents documents as mixtures over sparse topic vectors similar to LDA to maintain interpretability. This allows it to predict words based on local context and global document content.
Semantic Segmentation - Fully Convolutional Networks for Semantic Segmentation岳華 杜
This document discusses several semantic segmentation methods using deep learning, including fully convolutional networks (FCNs), U-Net, and SegNet. FCNs were among the first to use convolutional networks for dense, pixel-wise prediction by converting classification networks to fully convolutional form and combining coarse and fine feature maps. U-Net and SegNet are encoder-decoder architectures that extract high-level semantic features from the input image and then generate pixel-wise predictions, with U-Net copying and cropping features and SegNet using pooling indices for upsampling. These methods demonstrate that convolutional networks can effectively perform semantic segmentation through dense prediction.
Background subtraction is a technique used to separate foreground objects from backgrounds in video frames. It works by comparing each frame to a background model and detecting differences which indicate moving foreground objects. Recursive techniques like mixtures of Gaussians model the background pixel values over time using multiple Gaussian distributions, allowing the background model to adapt to changing lighting conditions. Adaptive background/foreground detection uses a background model that evolves over time to distinguish foreground objects from the background in a robust way.
This document discusses various techniques for question answering and relation extraction in natural language processing. It provides an overview of question answering systems and approaches, including examples like START, Ask Jeeves and Siri. It also discusses using search engines for question answering, relation extraction from questions, and common evaluation metrics for question answering systems like accuracy and mean reciprocal rank.
The document discusses recommender systems and describes several techniques used in collaborative filtering recommender systems including k-nearest neighbors (kNN), singular value decomposition (SVD), and similarity weights optimization (SWO). It provides examples of how these techniques work and compares kNN to SWO. The document aims to explain state-of-the-art recommender system methods.
This document discusses motion detection at night. It notes that while motion detection during daylight has been well-researched, nighttime detection has received less attention due to low-light conditions making objects less visible. Vehicle headlights can also affect background subtraction methods by changing the brightness of surrounding areas. The document proposes considering only dense bright regions corresponding to vehicle headlights to detect motion. It will use a video captured with an infrared camera and software-based image processing techniques like filtering, thresholding and background subtraction to detect and classify moving vehicles at night.
The document discusses different knowledge representation schemes used in artificial intelligence systems. It describes semantic networks, frames, propositional logic, first-order predicate logic, and rule-based systems. For each technique, it provides facts about how knowledge is represented and examples to illustrate their use. The goal of knowledge representation is to encode knowledge in a way that allows inferencing and learning of new knowledge from the facts stored in the knowledge base.
Multi-object tracking is a computer vision task which can track objects belonging to different categories, such as cars, pedestrians and animals by analyzing the videos.
Region-based image segmentation refers to partitioning an image into regions based on properties like color and texture. The goal is to simplify the image into meaningful regions that correspond to objects or parts of objects. Common approaches include region growing which starts from seed pixels and aggregates neighboring pixels with similar properties, and split-and-merge which first over-segments the image and then merges similar adjacent regions.
Recommender Systems represent one of the most widespread and impactful applications of predictive machine learning models.
Amazon, YouTube, Netflix, Facebook and many other companies generate an important fraction of their revenues thanks to their ability to model and accurately predict users ratings and preferences.
In this presentation we cover the following points:
→ introduction to recommender systems
→ working with explicit vs implicit feedback
→ content-based vs collaborative filtering approaches
→ user-based and item-item methods
→ machine learning and deep learning models
→ pros & cons of the methods: scalability, accuracy, explainability
[Paper Review] Personalized Top-N Sequential Recommendation via Convolutional...Jihoo Kim
This document summarizes a research paper on personalized top-N sequential recommendation using convolutional sequence embedding. The paper proposes a model called Caser that uses horizontal and vertical convolutional filters to capture sequential patterns at different levels from user behavior data. Caser outperforms previous methods by modeling both general user preferences and sequential patterns in a unified framework. The document provides details on Caser's network architecture, training approach, and evaluation on real-world datasets showing it achieves better performance than baseline methods.
K-Nearest neighbor is one of the most commonly used classifier based in lazy learning. It is one of the most commonly used methods in recommendation systems and document similarity measures. It mainly uses Euclidean distance to find the similarity measures between two data points.
This document discusses edge detection and image segmentation techniques. It begins with an introduction to segmentation and its importance. It then discusses edge detection, including edge models like steps, ramps, and roofs. Common edge detection techniques are described, such as using derivatives and filters to detect discontinuities that indicate edges. Point, line, and edge detection are explained through the use of filters like Laplacian filters. Thresholding techniques are introduced as a way to segment images into different regions based on pixel intensity values.
YOLOv4: optimal speed and accuracy of object detection reviewLEE HOSEONG
YOLOv4 builds upon previous YOLO models and introduces techniques like CSPDarknet53, SPP, PAN, Mosaic data augmentation, and modifications to existing methods to achieve state-of-the-art object detection speed and accuracy while being trainable on a single GPU. Experiments show that combining these techniques through a "bag of freebies" and "bag of specials" approach improves classifier and detector performance over baselines on standard datasets. The paper contributes an efficient object detection model suitable for production use with limited resources.
This document discusses region-based image segmentation techniques. It introduces region growing, which groups similar pixels into larger regions starting from seed points. Region splitting and merging are also covered, where splitting starts with the whole image as one region and splits non-homogeneous regions, while merging combines similar adjacent regions. The advantages of these methods are that they can correctly separate regions with the same properties and provide clear edge segmentation, while the disadvantages include being computationally expensive and sensitive to noise.
Mask R-CNN is an algorithm for instance segmentation that builds upon Faster R-CNN by adding a branch for predicting masks in parallel with bounding boxes. It uses a Feature Pyramid Network to extract features at multiple scales, and RoIAlign instead of RoIPool for better alignment between masks and their corresponding regions. The architecture consists of a Region Proposal Network for generating candidate object boxes, followed by two branches - one for classification and box regression, and another for predicting masks with a fully convolutional network using per-pixel sigmoid activations and binary cross-entropy loss. Mask R-CNN achieves state-of-the-art performance on standard instance segmentation benchmarks.
The document is a presentation on natural language processing (NLP) given by Bina Gupta. It discusses the definition of NLP as a subfield of AI that aims to build computers that can interact with humans like humans interact with each other. The presentation outlines applications of NLP techniques like automatic summarization, information extraction, grammar testing, question answering, and machine translation. It also discusses natural language understanding and the stages of morphological, syntactic, semantic, pragmatic, and discourse analysis required for a machine to understand human language. The presentation concludes with discussing the future of NLP in areas like the semantic web, sentiment analysis, and improved machine translation.
Image filtering in Digital image processingAbinaya B
This document discusses various image filtering techniques used for modifying or enhancing digital images. It describes spatial domain filters such as smoothing filters including averaging and weighted averaging filters, as well as order statistics filters like median filters. It also covers frequency domain filters including ideal low pass, Butterworth low pass, and Gaussian low pass filters for smoothing, as well as their corresponding high pass filters for sharpening. Examples of applying different filters at different cutoff frequencies are provided to illustrate their effects.
This document provides an overview of natural language processing (NLP) for text categorization and classification. It discusses supervised and unsupervised learning problems and classification algorithms like Naive Bayes and support vector machines (SVM). Specific applications mentioned include email classification, spam filtering, and document organization. The document compares Naive Bayes and SVM, noting that Naive Bayes is easier and faster while SVM is more difficult but can handle binary classification problems.
This document provides an overview of a course on computer vision called CSCI 455: Intro to Computer Vision. It acknowledges that many of the course slides were modified from other similar computer vision courses. The course will cover topics like image filtering, projective geometry, stereo vision, structure from motion, face detection, object recognition, and convolutional neural networks. It highlights current applications of computer vision like biometrics, mobile apps, self-driving cars, medical imaging, and more. The document discusses challenges in computer vision like viewpoint and illumination variations, occlusion, and local ambiguity. It emphasizes that perception is an inherently ambiguous problem that requires using prior knowledge about the world.
This presentation discusses computer vision techniques for human tracking and interaction. It begins with an outline of the topics to be covered, including basic visual tracking, multi-cue particle filtering for tracking, multi-human tracking, multi-camera tracking, and handling re-entering people. It then describes implementations of basic color-based tracking, particle filtering with multiple cues, and using particle filtering for human head tracking. Challenges with overlapping people are addressed through joint candidate evaluation and sorting by depth. The multi-camera system correlates tracks across cameras to identify corresponding people. Overall, the presentation explains a complete visual tracking and surveillance system using computer vision algorithms.
Matrix Factorization Techniques For Recommender SystemsLei Guo
The document discusses matrix factorization techniques for recommender systems. It begins by describing common recommender system strategies like content-based and collaborative filtering approaches. It then introduces matrix factorization methods, which characterize both users and items by vectors of latent factors inferred from rating patterns. The basic matrix factorization model approximates user ratings as the inner product of user and item vectors in the joint latent factor space. Learning algorithms like stochastic gradient descent and alternating least squares are used to compute the user and item vectors by minimizing a regularized error function on known ratings.
This document discusses techniques for image compression including bit-plane coding, bit-plane decomposition, constant area coding, and run-length coding. It explains that bit-plane decomposition represents a grayscale image as a collection of binary images based on its representation as a binary polynomial. Run-length coding compresses each row of a binary image by coding contiguous runs of 0s or 1s with their length, separately for black and white runs. Constant area coding classifies blocks of pixels as all white, all black, or mixed and codes them with special codewords.
Dr. kiani artificial neural network lecture 1Parinaz Faraji
The document provides a history of neural networks, beginning with McCulloch and Pitts creating the first neural network model in 1943. It then discusses several important developments in neural networks including perceptrons in the 1950s and 1960s, backpropagation in the 1980s, and neural networks being implemented in semiconductors in the late 1980s. The document also includes diagrams and explanations of biological neurons, artificial neurons, different types of activation functions, and key aspects of neural network architectures.
This document discusses various types of digital media including short films, promotional videos, film trailers, user-generated content, viral marketing, and advertising. It explains common platforms for sharing this content such as YouTube, social media sites, cinema, TV, and more. It also covers topics like video formats, downloading, streaming, data transfer rates, video resolution, and other technical aspects of digital video.
Video Captioning: How-To & Other ResourcesKeira Dooley
Captions are text versions of spoken audio that can be added to videos. They make media accessible for deaf or hard of hearing users and help all users comprehend content better. There are different types of captions like closed captions that are built into players and open captions that are permanently displayed. Captions should be synchronized with audio, equivalent to what is said, and accessible. Videos on YouTube can be captioned by uploading a caption file. Other options for captioning include CaptionTube, Overstream, Camtasia, and outsourcing to a captioning company.
The document discusses different knowledge representation schemes used in artificial intelligence systems. It describes semantic networks, frames, propositional logic, first-order predicate logic, and rule-based systems. For each technique, it provides facts about how knowledge is represented and examples to illustrate their use. The goal of knowledge representation is to encode knowledge in a way that allows inferencing and learning of new knowledge from the facts stored in the knowledge base.
Multi-object tracking is a computer vision task which can track objects belonging to different categories, such as cars, pedestrians and animals by analyzing the videos.
Region-based image segmentation refers to partitioning an image into regions based on properties like color and texture. The goal is to simplify the image into meaningful regions that correspond to objects or parts of objects. Common approaches include region growing which starts from seed pixels and aggregates neighboring pixels with similar properties, and split-and-merge which first over-segments the image and then merges similar adjacent regions.
Recommender Systems represent one of the most widespread and impactful applications of predictive machine learning models.
Amazon, YouTube, Netflix, Facebook and many other companies generate an important fraction of their revenues thanks to their ability to model and accurately predict users ratings and preferences.
In this presentation we cover the following points:
→ introduction to recommender systems
→ working with explicit vs implicit feedback
→ content-based vs collaborative filtering approaches
→ user-based and item-item methods
→ machine learning and deep learning models
→ pros & cons of the methods: scalability, accuracy, explainability
[Paper Review] Personalized Top-N Sequential Recommendation via Convolutional...Jihoo Kim
This document summarizes a research paper on personalized top-N sequential recommendation using convolutional sequence embedding. The paper proposes a model called Caser that uses horizontal and vertical convolutional filters to capture sequential patterns at different levels from user behavior data. Caser outperforms previous methods by modeling both general user preferences and sequential patterns in a unified framework. The document provides details on Caser's network architecture, training approach, and evaluation on real-world datasets showing it achieves better performance than baseline methods.
K-Nearest neighbor is one of the most commonly used classifier based in lazy learning. It is one of the most commonly used methods in recommendation systems and document similarity measures. It mainly uses Euclidean distance to find the similarity measures between two data points.
This document discusses edge detection and image segmentation techniques. It begins with an introduction to segmentation and its importance. It then discusses edge detection, including edge models like steps, ramps, and roofs. Common edge detection techniques are described, such as using derivatives and filters to detect discontinuities that indicate edges. Point, line, and edge detection are explained through the use of filters like Laplacian filters. Thresholding techniques are introduced as a way to segment images into different regions based on pixel intensity values.
YOLOv4: optimal speed and accuracy of object detection reviewLEE HOSEONG
YOLOv4 builds upon previous YOLO models and introduces techniques like CSPDarknet53, SPP, PAN, Mosaic data augmentation, and modifications to existing methods to achieve state-of-the-art object detection speed and accuracy while being trainable on a single GPU. Experiments show that combining these techniques through a "bag of freebies" and "bag of specials" approach improves classifier and detector performance over baselines on standard datasets. The paper contributes an efficient object detection model suitable for production use with limited resources.
This document discusses region-based image segmentation techniques. It introduces region growing, which groups similar pixels into larger regions starting from seed points. Region splitting and merging are also covered, where splitting starts with the whole image as one region and splits non-homogeneous regions, while merging combines similar adjacent regions. The advantages of these methods are that they can correctly separate regions with the same properties and provide clear edge segmentation, while the disadvantages include being computationally expensive and sensitive to noise.
Mask R-CNN is an algorithm for instance segmentation that builds upon Faster R-CNN by adding a branch for predicting masks in parallel with bounding boxes. It uses a Feature Pyramid Network to extract features at multiple scales, and RoIAlign instead of RoIPool for better alignment between masks and their corresponding regions. The architecture consists of a Region Proposal Network for generating candidate object boxes, followed by two branches - one for classification and box regression, and another for predicting masks with a fully convolutional network using per-pixel sigmoid activations and binary cross-entropy loss. Mask R-CNN achieves state-of-the-art performance on standard instance segmentation benchmarks.
The document is a presentation on natural language processing (NLP) given by Bina Gupta. It discusses the definition of NLP as a subfield of AI that aims to build computers that can interact with humans like humans interact with each other. The presentation outlines applications of NLP techniques like automatic summarization, information extraction, grammar testing, question answering, and machine translation. It also discusses natural language understanding and the stages of morphological, syntactic, semantic, pragmatic, and discourse analysis required for a machine to understand human language. The presentation concludes with discussing the future of NLP in areas like the semantic web, sentiment analysis, and improved machine translation.
Image filtering in Digital image processingAbinaya B
This document discusses various image filtering techniques used for modifying or enhancing digital images. It describes spatial domain filters such as smoothing filters including averaging and weighted averaging filters, as well as order statistics filters like median filters. It also covers frequency domain filters including ideal low pass, Butterworth low pass, and Gaussian low pass filters for smoothing, as well as their corresponding high pass filters for sharpening. Examples of applying different filters at different cutoff frequencies are provided to illustrate their effects.
This document provides an overview of natural language processing (NLP) for text categorization and classification. It discusses supervised and unsupervised learning problems and classification algorithms like Naive Bayes and support vector machines (SVM). Specific applications mentioned include email classification, spam filtering, and document organization. The document compares Naive Bayes and SVM, noting that Naive Bayes is easier and faster while SVM is more difficult but can handle binary classification problems.
This document provides an overview of a course on computer vision called CSCI 455: Intro to Computer Vision. It acknowledges that many of the course slides were modified from other similar computer vision courses. The course will cover topics like image filtering, projective geometry, stereo vision, structure from motion, face detection, object recognition, and convolutional neural networks. It highlights current applications of computer vision like biometrics, mobile apps, self-driving cars, medical imaging, and more. The document discusses challenges in computer vision like viewpoint and illumination variations, occlusion, and local ambiguity. It emphasizes that perception is an inherently ambiguous problem that requires using prior knowledge about the world.
This presentation discusses computer vision techniques for human tracking and interaction. It begins with an outline of the topics to be covered, including basic visual tracking, multi-cue particle filtering for tracking, multi-human tracking, multi-camera tracking, and handling re-entering people. It then describes implementations of basic color-based tracking, particle filtering with multiple cues, and using particle filtering for human head tracking. Challenges with overlapping people are addressed through joint candidate evaluation and sorting by depth. The multi-camera system correlates tracks across cameras to identify corresponding people. Overall, the presentation explains a complete visual tracking and surveillance system using computer vision algorithms.
Matrix Factorization Techniques For Recommender SystemsLei Guo
The document discusses matrix factorization techniques for recommender systems. It begins by describing common recommender system strategies like content-based and collaborative filtering approaches. It then introduces matrix factorization methods, which characterize both users and items by vectors of latent factors inferred from rating patterns. The basic matrix factorization model approximates user ratings as the inner product of user and item vectors in the joint latent factor space. Learning algorithms like stochastic gradient descent and alternating least squares are used to compute the user and item vectors by minimizing a regularized error function on known ratings.
This document discusses techniques for image compression including bit-plane coding, bit-plane decomposition, constant area coding, and run-length coding. It explains that bit-plane decomposition represents a grayscale image as a collection of binary images based on its representation as a binary polynomial. Run-length coding compresses each row of a binary image by coding contiguous runs of 0s or 1s with their length, separately for black and white runs. Constant area coding classifies blocks of pixels as all white, all black, or mixed and codes them with special codewords.
Dr. kiani artificial neural network lecture 1Parinaz Faraji
The document provides a history of neural networks, beginning with McCulloch and Pitts creating the first neural network model in 1943. It then discusses several important developments in neural networks including perceptrons in the 1950s and 1960s, backpropagation in the 1980s, and neural networks being implemented in semiconductors in the late 1980s. The document also includes diagrams and explanations of biological neurons, artificial neurons, different types of activation functions, and key aspects of neural network architectures.
This document discusses various types of digital media including short films, promotional videos, film trailers, user-generated content, viral marketing, and advertising. It explains common platforms for sharing this content such as YouTube, social media sites, cinema, TV, and more. It also covers topics like video formats, downloading, streaming, data transfer rates, video resolution, and other technical aspects of digital video.
Video Captioning: How-To & Other ResourcesKeira Dooley
Captions are text versions of spoken audio that can be added to videos. They make media accessible for deaf or hard of hearing users and help all users comprehend content better. There are different types of captions like closed captions that are built into players and open captions that are permanently displayed. Captions should be synchronized with audio, equivalent to what is said, and accessible. Videos on YouTube can be captioned by uploading a caption file. Other options for captioning include CaptionTube, Overstream, Camtasia, and outsourcing to a captioning company.
The document discusses various options for using video and web-based instruction. It recommends rethinking how video is produced, edited, and distributed for educational purposes. Some key points covered include using low-cost or freely available video content, student-produced videos, copyright issues, and distributing videos through streaming servers or services like YouTube.
Presentation from OpenCms Days 2014.
This session covers how we at componio accomplished turning OpenCms 9 into a video tube and which pitfalls we fell into but you should avoid.
You will learn how different software products such as OpenCms, JWplayer and ownCloud make for a nice video delivery platform. Moreover we will touch OpenCms features such as the subscription engine, the content relationship engine and customized detail pages together with an extension to Metamesh's RFS-Driver.
All these ingredients form the foundation for individually crafted but automatically aggregated video tubes. The final result is not as powerful as the well-known video portals but nonetheless a very good offering for OpenCms-centric installations.
Video Production Using Open Source ToolsCrazed Mule
Abstract: Over the last decade, farms of Linux servers have powered the production of major motion pictures. Today, individuals can use Linux to produce and distribute video in numerous formats; for example, YouTube, iTunes, DVD, and Blu Ray. Linux is no longer a hobbyists' tool, but a powerful production system that can be custom tailored. However, setting up a system like this is not for the faint of heart. Video and audio encoding and compression schemes can drive one to drink. Editing software in Linux is not polished, but difficulties can be overcome with perseverance. I will attempt to show how to create a working production workflow using Fedora, Cinelerra and various open source tools to produce a video ready for YouTube, iTunes, DVD and Vimeo.
This document provides information and instructions for transcribing audio and video content using Audio Notetaker software. It discusses difficulties in working with various video formats, using an audio typist or Audio Notetaker for transcription, capturing both audio and screenshots. Instructions are provided for transcription, output formats, copyright issues, subtitling videos on YouTube, converting DVDs, and adding closed captions without transcripts.
Beef Up Your Website With Audio And Video - It's Easy!Melodie Laylor
This document is a presentation about adding audio and video to websites. It discusses why multimedia grabs attention and drives traffic. It covers audio and video basics like recommended file formats and software for recording and editing. It recommends hosting audio and video files externally rather than on a website server. The presentation explores linking multimedia through quick methods, plugins, and podcasting. It highlights the Blubrry PowerPress plugin for easy podcasting and iTunes submission.
This project was a part of my course Integrating Information Systems Technologies. Here, I have tried to create a software service for the production houses to dub a movie/video in multiple languages using the original voice of the artist.
Slide number 17 and 18 can be viewed when the presentation is downloaded. It contains videos as examples.
The document discusses video podcasting and provides information on creating and hosting video podcasts. It defines key terms like vlogging and discusses trends in online video. It also provides tips on creating short, engaging video podcasts and recommends free software like VirtualDub for editing video and hosting sites like blip.tv for hosting video podcasts.
Remote video production is a term used to describe the process of producing and/or live streaming an event with multiple locations from a single studio. Today’s latest live streaming and video conferencing technology is allowing content creators from all walks of life to communicate and create video content that can connect with multiple locations from all around the world. The possible use cases for video conferencing alone are still being discovered every day. The same is true for live streaming. But when both emerging technologies are used together, something pretty amazing can happen.
In the video above, you will see a demonstration of our a live stream hosting in Avon, Ohio but produced in our offices outside Philadelphia, Pennsylvania. If you follow the diagram above you can see a simple video conferencing connection using the lifesize cloud was used to connect both sites. This was actually the first ever live stream for Jenne Inc (a professional AV distribution company) and having PTZOptics handle the video production was as simple as joining a video call. Patrick Kirby and Amy Frantz-Wolf even put up a green screen allowing our team to add a virtual background.
The applications for this technology range from talk shows and advanced webinars with massive attendance potentials to video production as a service for professional value add integration companies. Both live streaming and video conferencing technologies in general benefit from wide user adoption and appeal across major market verticals.
Our video blog and live shows every Friday focus on these emerging technologies. If you are interested in learning more subscribe to our YouTube channel and download one of our detailed guides on plug and play live streaming. If you have a individual at your office who wants to start working with this powerful technology send them a free link to our UDEMY courses which feature basic, intermediate and advanced courses on how to livestream webinars, talk shows and much more!
Closed Captioning Legal Requirements, Best Practices, and Workflows for Media...3Play Media
New FCC regulations require video programming that is captioned on TV to also be captioned when distributed on the Internet. In this webinar, thePlatform will join us to discuss recent and upcoming regulations for closed captioning, as well as best practices for implementation and workflows.
How did you use media technologies in the construction and research, planning...John Smith
Connor O'Reilly used various media technologies throughout the construction, research, planning, and evaluation of their short film. They used a Canon DSLR camera to film scenes, Adobe Premiere Pro for editing, Photoshop to create graphics, and Sound Booth to edit audio. Additional technologies like SlideShare, Vimeo, and YouTube were used for research and sharing work. Throughout the process, these tools were crucial for filming, editing, presenting work, and completing the short film and ancillary tasks.
Veoh is an internet television service that allows users to broadcast and watch videos online. It uses peer-to-peer distribution technology to broadcast high quality videos quickly. Unlike sites like YouTube that are mainly for short videos, Veoh allows videos that can be hours long. It is considered a form of "internet TV" that brings together multiple video sources into one application and allows users to access content from their PC, TV, or portable devices. Veoh makes money through advertisements and revenue sharing from rentals and purchases of user-uploaded videos.
Optimising video delivery - Brightcove PLAY 2019Jeremy Brown
7plus has taken advantage of the flexibility of the Brightcove video platform using the latest features Context Aware Encoding (CAE), Delivery Rules driven when driven by the 7plus automated quality assessment (QA) ingest workflow to mitigate common compromises to deliver better video experiences for our users
I presented at Brightcove PLAY May 2019 (suffering severe jet lag), thanks to everyone involved this was a fantastic event
Web 2.0 applications have enabled live streaming and screencasting for businesses and individuals. The document discusses tools like Stickam, Tinychat, Ustream, and Skype that allow live video broadcasting from computers and mobile devices. It also covers features of live streaming like embedding players, importing media, and interactive chat. Live streaming provides opportunities for businesses to engage customers in real-time and create archived content.
Speaker Recording Tips For Virtual DevOps Enterprise (And Why We're Pre-Recor...Gene Kim
In this presentation, I describe why we've decided to pre-record our talks for DevOps Enterprise Summit, and some of the top lessons learned for any speaker who needs to record their presentations.
I cover microphones, standing up, elevating your camera, adjusting your lighting, picking a good background, and record!
To learn more about the awesome DevOps Enterprise Summit programming here: https://itrevolution.com/london-virtual-what-to-expect/
What considerations need to be considered in order to make video accessible to all users? This presentation considers the law, standards and ways to make your video more accessible when used online.
Creating videos in the classroom can be an engaging way for students to learn. There are several free and easy to use video editing programs like Windows Movie Maker and iMovie that allow quick video editing. Incorporating video into lessons can increase student comprehension, from 10% for reading to 95% for teaching others. Copyright issues should be considered when adding music or images to videos. [/SUMMARY]
Web 2.0 applications have enabled live streaming and screencasting capabilities for businesses and individuals. These tools allow for real-time broadcasting as well as archived recordings. Popular platforms have converged features of chatrooms, radio, television, and blogs to offer synchronous and asynchronous streaming options like embedded players and mobile capture. Businesses can now easily reach customers live or share past presentations through these personalized digital communications channels.
My presentation about WordPress and caching from WordCamp Baltimore 2013.
See it with funny animated GIFs at http://kingkool68.com/wp-cream/
Fork my slides on GitHub https://github.com/kingkool68/WP-Cache-Rules-Everything-Around-Me
stickyHeader.js is a script I wrote to make table headers stick to the top of the viewport of a browser when scrolling down them. This makes it easier to understand the data. This presentation walks you through how I built it.
The code is available at https://github.com/kingkool68/stickyHeader
This document discusses how analytics can help website owners better understand their audience through data. It provides an overview of common analytics terminology like visits, unique visitors, pageviews, bounce rate, and landing pages. It also recommends Google Analytics as a good free analytics package and includes examples of how analytics data from a blog can be analyzed to draw conclusions and inform improvements. Plugins for analytics integration in WordPress are also highlighted.
Practical tips to make a website more accessible to different devices, technologies, and interactions. Presented April 12, 2011 for the WordPress DC Meetup.
A brief run through of the various APIs Google offers for creating free interactive and static data visualizations.
Links mentioned in this presentation: http://dev.kingkool68.com/google-charting-api/list-o-links.html
The document discusses building accessible websites from the ground up. It emphasizes that accessibility is about making websites usable for all people, regardless of ability or device. It provides tips for making websites accessible, such as using semantic HTML, adding text alternatives to images, ensuring keyboard and screen reader navigation works properly, and using ARIA roles to define page structure. The document stresses that accessibility benefits users and search engines alike.
This presentation was provided by Steph Pollock of The American Psychological Association’s Journals Program, and Damita Snow, of The American Society of Civil Engineers (ASCE), for the initial session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session One: 'Setting Expectations: a DEIA Primer,' was held June 6, 2024.
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
Executive Directors Chat Leveraging AI for Diversity, Equity, and InclusionTechSoup
Let’s explore the intersection of technology and equity in the final session of our DEI series. Discover how AI tools, like ChatGPT, can be used to support and enhance your nonprofit's DEI initiatives. Participants will gain insights into practical AI applications and get tips for leveraging technology to advance their DEI goals.
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The simplified electron and muon model, Oscillating Spacetime: The Foundation...RitikBhardwaj56
Discover the Simplified Electron and Muon Model: A New Wave-Based Approach to Understanding Particles delves into a groundbreaking theory that presents electrons and muons as rotating soliton waves within oscillating spacetime. Geared towards students, researchers, and science buffs, this book breaks down complex ideas into simple explanations. It covers topics such as electron waves, temporal dynamics, and the implications of this model on particle physics. With clear illustrations and easy-to-follow explanations, readers will gain a new outlook on the universe's fundamental nature.
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Thinking of getting a dog? Be aware that breeds like Pit Bulls, Rottweilers, and German Shepherds can be loyal and dangerous. Proper training and socialization are crucial to preventing aggressive behaviors. Ensure safety by understanding their needs and always supervising interactions. Stay safe, and enjoy your furry friends!
2. Russell Heimlich
★ Sole developer at the Pew Research Center
★ Creator of dummyimage.com
★ Frontend and Backend Developer
★ I care about accessibility
3. What Are Video Captions?
“Captions are text versions of the spoken
word allowing the content of web audio
and video to be accessible to those who
do not have access to audio.”
– WebAim.org
4. What Are Video Captions?
★ You know them as the text on the bottom
5. Captions vs. Subtitles
★ Captions are a transcript of the audio and key
sound effects for deaf viewers.
★ Subtitles are translations of the audio in another
language for hearing viewers.
★ http://joeclark.org/appearances/AEA/2007/
6. Open Captions vs. Closed Captions
★ Open captions are burned in to the video and
always on the screen.
★ Closed captions can be turned on or off and are
independent of the video.
7. Open Captions vs. Closed Captions
★ Open captions are like a flattened image
★ Closed captions are like Photoshop layers
8. Who Benefits from Captions?
★ Deaf viewers
★ Hard of hearing
★ Second language
learners
★ Anyone watching TV
in a noisy
environment
★ Machines (online)
9. The History of Captions
How we got to where we are today...
10. PBS’ French Chef (1972)
★ First television program that was accessible to
deaf and hard of hearing viewers.
★ Used “Open” Captions (burned onto the video)
★ Source: http://www.ncicap.org/caphist.asp
11. Closed Captioning
★ First demonstrated in 1971 at a Hearing
Impaired conference in Nashville.
★ 2nd Demo at Gallaudet College (now Gallaudet
University) on February 15, 1972.
★ PBS station WETA broadcasted the first closed
captioned programming in 1973.
12. The Early Years of Closed Captioning
★ Real-time closed captioning wasn’t available
until 1982.
★ A separate set-top box was needed to decode
13. Television Decoder Circuitry Act of 1990
★ Gave FCC power to enact rules on the
implementation of Closed Captioning.
★ Required screens 13” or greater to have built-in
chip to display closed captions.
★ Enforced on July 1st, 1993
14. 1990 Americans with Disabilities Act
★ Ensures equal opportunity for persons with
disabilities
★ Public facilities (excluding movie theaters) had
to provide access to verbal information on
televisions, films or slide shows
15. Telecommunications Act of 1996
Requires people or companies that distribute
television programs directly to home viewers to
make sure those programs are captioned by
January 1, 1998.
Source: National Institute on Deafness and Other
Communication Disorders
16. 21st Century Communications and Video
Accessibility Act of 2010
★ Requires broadcasters to provide captioning for
television programs redistributed on the web.
★ Source: Bill H.R. 3101
18. By Online I Really Mean YouTube
★ YouTube receives 48 hours of video a minute
★ 3 Billion views a day!
★ According to ComScore, as of April 2011,
YouTube is the top online video property.
★ Source: YouTube Blog & ComScore
34. Upload Your Own
★ Supports SubViewer (.sub) and SubRip (.srt)
★ YouTube has it’s own similar format called SBV
★ Any of these can be created in a text editor
★ YouTube will convert it to SBV for you!
35. SubViewer (.sub) Format
{Start frame}{End frame}Text ( | = line break )
{1471}{1538}..and the continuance|of their parents' rage,...
{1540}{1634}..which, but their children's end,|nought could
remove,...
{1636}{1702}..is now the two hours' traffic|of our stage.
36. SubRip (.srt) Format
Subtitle Number
Start time --> End time (HH:MM:SS,milliseconds)
Text (one or more lines)
Blank line
1
00:00:20,000 --> 00:00:24,400
Altocumulus clouds occur between six thousand
2
00:00:24,600 --> 00:00:27,800
and twenty thousand feet above ground level.
37. YouTube’s SBV Format
Start time, End time (H:MM:SS.milliseconds)
Text (one or more lines)
0:00:03.490,0:00:07.430
>> FISHER: All right. So, let's begin.
This session is: Going Social
0:00:07.430,0:00:11.600
with the YouTube APIs. I am
Jeff Fisher,
0:00:14.009,0:00:15.889
[pause]
38. Upload A Transcript
★ No timecode? No problem.
★ Upload a transcription and YouTube will sync it
to the video automatically
★ English and Japanese Only
39. Automatic Transcriptions
★ Uses speech recognition to auto-caption video
★ Same quality as Google Voice Transcriptions
★ Manually Started (could take a few days)
★ Source: googlesystem.blogspot.com
42. YouTube Caption Limitations
★ You can only add captions to your own videos!
★ Poor audio quality = poor caption quality
★ Caption data only available via API to
logged-in users
43. Other YouTube Caption Tricks
★ Add ,cc to any search to show only captioned
videos
★ In the player, press...
+ to increase font
- to decrese
B or b to toggle caption background
★ Captions are repositionable (YouTube.com only)
45. ★ Video captions are important
★ Plenty of services to do caption videos for you
★ Not a lot of good tools available
★ Tedious to create captions from scratch today
★ YouTube is easier/cheap way to caption videos