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| analysis of a video: Content-Based Analysis of Digital Video Alan Hanjalic, 2007-05-08 Content-Based Analysis Of Digital Video focuses on fundamental issues underlying the development of content access mechanisms for digital video. It treats topics that are critical to successfully automating the video content extraction and retrieval processes, and includes coverage of: - Video parsing, - Video content indexing and representation, - Affective video content analysis. In this well illustrated book the author integrates related information currently scattered throughout the literature and combines it with new ideas into a unified theoretical approach to video content analysis. The material also suggests ideas for future research. Systems developers, researchers and students working in the area of content-based analysis and retrieval of video and multimedia in general will find this book invaluable. |
| analysis of a video: Analysis I Terence Tao, 2016-08-29 This is part one of a two-volume book on real analysis and is intended for senior undergraduate students of mathematics who have already been exposed to calculus. The emphasis is on rigour and foundations of analysis. Beginning with the construction of the number systems and set theory, the book discusses the basics of analysis (limits, series, continuity, differentiation, Riemann integration), through to power series, several variable calculus and Fourier analysis, and then finally the Lebesgue integral. These are almost entirely set in the concrete setting of the real line and Euclidean spaces, although there is some material on abstract metric and topological spaces. The book also has appendices on mathematical logic and the decimal system. The entire text (omitting some less central topics) can be taught in two quarters of 25–30 lectures each. The course material is deeply intertwined with the exercises, as it is intended that the student actively learn the material (and practice thinking and writing rigorously) by proving several of the key results in the theory. |
| analysis of a video: Video Interaction Analysis Ulrike Tikvah Kissmann, 2009 This volume presents a collection of approaches to the emerging field of video analysis in the social sciences. Although the importance of visual qualitative methods has increased, video analysis cannot draw upon a single method or methodology. Therefore this book will structure the diverse approaches in order to identify their traditions. It assembles studies from linguistic anthropology as well as conversation analysis, sociological hermeneutics, ethnography, phenomenology and finally focused ethnography. Practical questions will be asked, as for instance, how the fact of being filmed affects the situation that is being filmed and theoretical questions will be posed, as for example, whether actions are subject to contingency or whether they are pre-determined. |
| analysis of a video: Video Analysis in Collision Reconstruction Mark Crouch, 2017 |
| analysis of a video: Foundations of Analysis Edmund Landau, 2021-02 Natural numbers, zero, negative integers, rational numbers, irrational numbers, real numbers, complex numbers, . . ., and, what are numbers? The most accurate mathematical answer to the question is given in this book. |
| analysis of a video: Physics and Video Analysis Rhett Allain, 2016-04-01 We currently live in a world filled with videos. There are videos on YouTube, feature movies and even videos recorded with our own cameras and smartphones. These videos present an excellent opportunity to not only explore physical concepts, but also inspire others to investigate physics ideas. With video analysis, we can explore the fantasy world in science-fiction films. We can also look at online videos to determine if they are genuine or fake. Video analysis can be used in the introductory physics lab and it can even be used to explore the make-believe physics embedded in video games. This book covers the basic ideas behind video analysis along with the fundamental physics principles used in video analysis. The book also includes several examples of the unique situations in which video analysis can be used. |
| analysis of a video: Machine Learning for Audio, Image and Video Analysis Francesco Camastra, Alessandro Vinciarelli, 2015-07-21 This second edition focuses on audio, image and video data, the three main types of input that machines deal with when interacting with the real world. A set of appendices provides the reader with self-contained introductions to the mathematical background necessary to read the book. Divided into three main parts, From Perception to Computation introduces methodologies aimed at representing the data in forms suitable for computer processing, especially when it comes to audio and images. Whilst the second part, Machine Learning includes an extensive overview of statistical techniques aimed at addressing three main problems, namely classification (automatically assigning a data sample to one of the classes belonging to a predefined set), clustering (automatically grouping data samples according to the similarity of their properties) and sequence analysis (automatically mapping a sequence of observations into a sequence of human-understandable symbols). The third part Applications shows how the abstract problems defined in the second part underlie technologies capable to perform complex tasks such as the recognition of hand gestures or the transcription of handwritten data. Machine Learning for Audio, Image and Video Analysis is suitable for students to acquire a solid background in machine learning as well as for practitioners to deepen their knowledge of the state-of-the-art. All application chapters are based on publicly available data and free software packages, thus allowing readers to replicate the experiments. |
| analysis of a video: Video Data Analysis Anne Nassauer, Nicolas M. Legewie, 2022-03-17 Video data is transforming the possibilities of social science research. Whether through mobile phone footage, body-worn cameras or public video surveillance, we have access to an ever-expanding pool of data on real-life situations and interactions. This book provides a flexible framework for working with video data and understanding what it says about social life. With examples from a range of real video research projects, the book showcases step-by-step how to analyse any kind of data, including both found and generated videos. It also includes a non-technical discussion of computer vision and its opportunities for social science research. With this book you will be able to: · Complete each step of the research process fully and efficiently, from data collection to management, analysis, and interpretation · Use video data in an ethical and effective way to maximise its impact · Utilise contemporary technology and accessible platforms such as YouTube, Twitter, Tik Tok and Facebook. This book is an ideal toolkit for researchers or postgraduate students across the social sciences working with video data as a part of their research projects. Accessible and practical, is written for qualitative and quantitative researchers, newcomers and experienced scholars. Features include interactive activities for different skill levels and ‘what to read next’ sections to help you engage further with the research mentioned in the book. |
| analysis of a video: Deep Learning for Computer Vision Rajalingappaa Shanmugamani, 2018-01-23 Learn how to model and train advanced neural networks to implement a variety of Computer Vision tasks Key Features Train different kinds of deep learning model from scratch to solve specific problems in Computer Vision Combine the power of Python, Keras, and TensorFlow to build deep learning models for object detection, image classification, similarity learning, image captioning, and more Includes tips on optimizing and improving the performance of your models under various constraints Book Description Deep learning has shown its power in several application areas of Artificial Intelligence, especially in Computer Vision. Computer Vision is the science of understanding and manipulating images, and finds enormous applications in the areas of robotics, automation, and so on. This book will also show you, with practical examples, how to develop Computer Vision applications by leveraging the power of deep learning. In this book, you will learn different techniques related to object classification, object detection, image segmentation, captioning, image generation, face analysis, and more. You will also explore their applications using popular Python libraries such as TensorFlow and Keras. This book will help you master state-of-the-art, deep learning algorithms and their implementation. What you will learn Set up an environment for deep learning with Python, TensorFlow, and Keras Define and train a model for image and video classification Use features from a pre-trained Convolutional Neural Network model for image retrieval Understand and implement object detection using the real-world Pedestrian Detection scenario Learn about various problems in image captioning and how to overcome them by training images and text together Implement similarity matching and train a model for face recognition Understand the concept of generative models and use them for image generation Deploy your deep learning models and optimize them for high performance Who this book is for This book is targeted at data scientists and Computer Vision practitioners who wish to apply the concepts of Deep Learning to overcome any problem related to Computer Vision. A basic knowledge of programming in Python—and some understanding of machine learning concepts—is required to get the best out of this book. |
| analysis of a video: The Bloomsbury Handbook of Popular Music Video Analysis Lori A. Burns, Stan Hawkins, 2019-10-17 Music videos promote popular artists in cultural forms that circulate widely across social media networks. With the advent of YouTube in 2005 and the proliferation of handheld technologies and social networking sites, the music video has become available to millions worldwide, and continues to serve as a fertile platform for the debate of issues and themes in popular culture. This volume of essays serves as a foundational handbook for the study and interpretation of the popular music video, with the specific aim of examining the industry contexts, cultural concepts, and aesthetic materials that videos rely upon in order to be both intelligible and meaningful. Easily accessible to viewers in everyday life, music videos offer profound cultural interventions and negotiations while traversing a range of media forms. From a variety of unique perspectives, the contributors to this volume undertake discussions that open up new avenues for exploring the creative changes and developments in music video production. With chapters that address music video authorship, distribution, cultural representations, mediations, aesthetics, and discourses, this study signals a major initiative to provide a deeper understanding of the intersecting and interdisciplinary approaches that are invoked in the analysis of this popular and influential musical form. |
| analysis of a video: Essentials of Interpretative Phenomenological Analysis Jonathan A. Smith, Isabella E. Nizza, 2021-08-31 The brief, practical texts in the Essentials of Qualitative Methods series introduce social science and psychology researchers to key approaches to to qualitative methods, offering exciting opportunities to gather in-depth qualitative data and to develop rich and useful findings. Essentials of Interpretative Phenomenological Analysis is a step-by-step guide to a research method that investigates how people make sense of their lived experience in the context of their personal and social worlds. It is especially well-suited to exploring experiences perceived as highly significant, such as major life and relationship changes, health challenges, and other emotion-laden events. IPA studies highlight convergence and divergence across participants, showing both the experiential themes that the participants share and the unique way each theme is manifested for the individual. About the Essentials of Qualitative Methods book series: Even for experienced researchers, selecting and correctly applying the right method can be challenging. In this groundbreaking series, leading experts in qualitative methods provide clear, crisp, and comprehensive descriptions of their approach, including its methodological integrity, and its benefits and limitations. Each book includes numerous examples to enable readers to quickly and thoroughly grasp how to leverage these valuable methods. |
| analysis of a video: Methods of Real Analysis Richard R. Goldberg, 2019-07-30 This is a textbook for a one-year course in analysis desighn for students who have completed the ordinary course in elementary calculus. |
| analysis of a video: Video Research in the Learning Sciences Ricki Goldman, Roy Pea, Brigid Barron, Sharon J. Derry, 2014-05-01 Video Research in the Learning Sciences is a comprehensive exploration of key theoretical, methodological, and technological advances concerning uses of digital video-as-data in the learning sciences as a way of knowing about learning, teaching, and educational processes. The aim of the contributors, a community of scholars using video in their own work, is to help usher in video scholarship and supportive technologies, and to mentor video scholars, so that video research will meet its maximum potential to contribute to the growing knowledge base about teaching and learning. This volume contributes deeply to both to the science of learning through in-depth video studies of human interaction in learning environments—whether classrooms or other contexts—and to the uses of video for creating descriptive, explanatory, or expository accounts of learning and teaching. It is designed around four themes—each with a cornerstone chapter that introduces and synthesizes the cluster of chapters related to it: Theoretical frameworks for video research; Video research on peer, family, and informal learning; Video research on classroom and teacher learning; and Video collaboratories and technological futures. Video Research in the Learning Sciences is intended for researchers, university faculty, teacher educators, and graduate students in education, and for anyone interested in how knowledge is expanded using video-based technologies for inquiries about learning and teaching. Visit the Web site affiliated with this book: www.videoresearch.org |
| analysis of a video: Video in Qualitative Research Christian Heath, Jon Hindmarsh, Paul Luff, 2010-03-12 Provides practical guidance for both students and academics on how to use video in qualitative research, how to address the problems and issues that arise in undertaking video-based field studies and how to subject video recordings to detailed scrutiny and analysis. |
| analysis of a video: Analyzing Qualitative Data with MAXQDA Udo Kuckartz, Stefan Rädiker, 2019-05-31 This book presents strategies for analyzing qualitative and mixed methods data with MAXQDA software, and provides guidance on implementing a variety of research methods and approaches, e.g. grounded theory, discourse analysis and qualitative content analysis, using the software. In addition, it explains specific topics, such as transcription, building a coding frame, visualization, analysis of videos, concept maps, group comparisons and the creation of literature reviews. The book is intended for masters and PhD students as well as researchers and practitioners dealing with qualitative data in various disciplines, including the educational and social sciences, psychology, public health, business or economics. |
| analysis of a video: Trading with Intermarket Analysis John J. Murphy, 2015-10-05 A visual guide to market trading using intermarket analysis and exchange-traded funds With global markets and asset classes growing even more interconnected, intermarket analysis—the analysis of related asset classes or financial markets to determine their strengths and weaknesses—has become an essential part of any trader's due diligence. In Trading with Intermarket Analysis, John J. Murphy, former technical analyst for CNBC, lays out the technical and intermarket tools needed to understand global markets and illustrates how they help traders profit in volatile climates using exchange-traded funds. Armed with a knowledge of how economic forces impact various markets and financial sectors, investors and traders can profit by exploiting opportunities in markets about to rise and avoiding those poised to fall. Trading with Intermarket Analysis provides advice on trend following, chart patterns, moving averages, oscillators, spotting tops and bottoms, using exchange-traded funds, tracking market sectors, and the new world of intermarket relationships, all presented in a highly visual way. Gives readers a visually rich introduction to the world of intermarket analysis, the ultimate tool for beating the markets Provides practical advice on trend following, chart patterns, moving averages, oscillators, spotting tops and bottoms, using exchange-traded funds, tracking market sectors, and intermarket relationships Includes appendices on Japanese candlesticks and point-and-figure charting Comprehensive and easy-to-use, Trading with Intermarket Analysis presents the most important concepts related to using exchange-traded funds to beat the markets in a visually accessible format. |
| analysis of a video: Video Ethnography in Practice Wesley Shrum, Greg Scott, 2016-11-04 Video Ethnography in Practice is a brief guide for students in the social disciplines who are required to produce an ethnographic video, the most significant new methodological technique in 21st century social analysis. It shows students at any level how to plan, shoot, and edit their own ethnographic videos within three weeks using desktop technology and widely available software. |
| analysis of a video: Security Analysis: Sixth Edition, Foreword by Warren Buffett Benjamin Graham, David Dodd, 2008-09-14 A road map for investing that I have now been following for 57 years. --From the Foreword by Warren E. Buffett First published in 1934, Security Analysis is one of the most influential financial books ever written. Selling more than one million copies through five editions, it has provided generations of investors with the timeless value investing philosophy and techniques of Benjamin Graham and David L. Dodd. As relevant today as when they first appeared nearly 75 years ago, the teachings of Benjamin Graham, “the father of value investing,” have withstood the test of time across a wide diversity of market conditions, countries, and asset classes. This new sixth edition, based on the classic 1940 version, is enhanced with 200 additional pages of commentary from some of today’s leading Wall Street money managers. These masters of value investing explain why the principles and techniques of Graham and Dodd are still highly relevant even in today’s vastly different markets. The contributor list includes: Seth A. Klarman, president of The Baupost Group, L.L.C. and author of Margin of Safety James Grant, founder of Grant's Interest Rate Observer, general partner of Nippon Partners Jeffrey M. Laderman, twenty-five year veteran of BusinessWeek Roger Lowenstein, author of Buffett: The Making of an American Capitalist and When America Aged and Outside Director, Sequoia Fund Howard S. Marks, CFA, Chairman and Co-Founder, Oaktree Capital Management L.P. J. Ezra Merkin, Managing Partner, Gabriel Capital Group . Bruce Berkowitz, Founder, Fairholme Capital Management. Glenn H. Greenberg, Co-Founder and Managing Director, Chieftain Capital Management Bruce Greenwald, Robert Heilbrunn Professor of Finance and Asset Management, Columbia Business School David Abrams, Managing Member, Abrams Capital Featuring a foreword by Warren E. Buffett (in which he reveals that he has read the 1940 masterwork “at least four times”), this new edition of Security Analysis will reacquaint you with the foundations of value investing—more relevant than ever in the tumultuous 21st century markets. |
| analysis of a video: Machine Learning Methods for Behaviour Analysis and Anomaly Detection in Video Olga Isupova, 2018-02-24 This thesis proposes machine learning methods for understanding scenes via behaviour analysis and online anomaly detection in video. The book introduces novel Bayesian topic models for detection of events that are different from typical activities and a novel framework for change point detection for identifying sudden behavioural changes. Behaviour analysis and anomaly detection are key components of intelligent vision systems. Anomaly detection can be considered from two perspectives: abnormal events can be defined as those that violate typical activities or as a sudden change in behaviour. Topic modelling and change-point detection methodologies, respectively, are employed to achieve these objectives. The thesis starts with the development of learning algorithms for a dynamic topic model, which extract topics that represent typical activities of a scene. These typical activities are used in a normality measure in anomaly detection decision-making. The book also proposes a novel anomaly localisation procedure. In the first topic model presented, a number of topics should be specified in advance. A novel dynamic nonparametric hierarchical Dirichlet process topic model is then developed where the number of topics is determined from data. Batch and online inference algorithms are developed. The latter part of the thesis considers behaviour analysis and anomaly detection within the change-point detection methodology. A novel general framework for change-point detection is introduced. Gaussian process time series data is considered. Statistical hypothesis tests are proposed for both offline and online data processing and multiple change point detection are proposed and theoretical properties of the tests are derived. The thesis is accompanied by open-source toolboxes that can be used by researchers and engineers. |
| analysis of a video: Semantic Analysis and Understanding of Human Behavior in Video Streaming Alberto Amato, Vincenzo Di Lecce, Vincenzo Piuri, 2012-09-18 Semantic Analysis and Understanding of Human Behaviour in Video Streaming investigates the semantic analysis of the human behaviour captured by video streaming, and introduces both theoretical and technological points of view. Video analysis based on the semantic content is in fact still an open issue for the computer vision research community, especially when real-time analysis of complex scenes is concerned. This book explores an innovative, original approach to human behaviour analysis and understanding by using the syntactical symbolic analysis of images and video streaming described by means of strings of symbols. A symbol is associated to each area of the analyzed scene. When a moving object enters an area, the corresponding symbol is appended to the string describing the motion. This approach allows for characterizing the motion of a moving object with a word composed by symbols. By studying and classifying these words we can categorize and understand the various behaviours. The main advantage of this approach lies in the simplicity of the scene and motion descriptions so that the behaviour analysis will have limited computational complexity due to the intrinsic nature both of the representations and the related operations used to manipulate them. Besides, the structure of the representations is well suited for possible parallel processing, thus allowing for speeding up the analysis when appropriate hardware architectures are used. A new methodology for design systems for hierarchical high semantic level analysis of video streaming in narrow domains is also proposed. Guidelines to design your own system are provided in this book. Designed for practitioners, computer scientists and engineers working within the fields of human computer interaction, surveillance, image processing and computer vision, this book can also be used as secondary text book for advanced-level students in computer science and engineering. |
| analysis of a video: Odyssey Homer, 2019 Since their composition almost 3,000 years ago the Homeric epics have lost none of their power to grip audiences and fire the imagination: with their stories of life and death, love and loss, war and peace they continue to speak to us at the deepest level about who we are across the span of generations. That being said, the world of Homer is in many ways distant from that in which we live today, with fundamental differences not only in language, social order, and religion, but in basic assumptions about the world and human nature. This volume offers a detailed yet accessible introduction to ancient Greek culture through the lens of Book One of the Odyssey, covering all of these aspects and more in a comprehensive Introduction designed to orient students in their studies of Greek literature and history. The full Greek text is included alongside a facing English translation which aims to reproduce as far as feasible the word order and sound play of the Greek original and is supplemented by a Glossary of Technical Terms and a full vocabulary keyed to the specific ways that words are used in Odyssey I. At the heart of the volume is a full-length line-by-line commentary, the first in English since the 1980s and updated to bring the latest scholarship to bear on the text: focusing on philological and linguistic issues, its close engagement with the original Greek yields insights that will be of use to scholars and advanced students as well as to those coming to the text for the first time. |
| analysis of a video: Technical Analysis Using Multiple Timeframes Brian Shannon, 2008-03-08 focuses on analyzing price charts across different timeframes to identify trends, key resistance and support levels, and potential trading opportunities. The book has 184 pages. Here are some key features of the book:The book emphasizes the importance of using multiple timeframes to analyze price charts and identify trading opportunities.It provides a detailed and practical approach to analyzing price charts across different timeframes, including weekly, daily, 30-minute, 15-minute, and 5-minute timeframes.The book covers a range of technical analysis tools and techniques, including volume moving averages, VWAP, and chart patterns.It provides guidance on how to anticipate price movements rather than react to them, which can help traders make more informed trading decisions.The book includes real-world examples and case studies to illustrate how the concepts and techniques discussed in the book can be applied in practice. |
| analysis of a video: Video Analysis: Methodology and Methods Hubert Knoblauch, Jürgen Raab, Hans-Georg Soeffner, Bernt Schnettler, 2012 This book gathers a selection of outstanding European researchers in the field of qualitative interpretive video analysis. The contributions discuss the crucial features of video data and present different approaches how to handle, interpret, analyse and present video data collected in a wide range of «real world» social fields. |
| analysis of a video: Coraline Neil Gaiman, 2008-01-01 A brilliant graphic novel adaptation of Neil Gaiman's critically acclaimed novel for young people. When Coraline moves to a new home, she is fascinated by the fact that the 'house' is really only half a house - it was divided into flats years before. And it soon becomes clear to Coraline that the other flat is not quite as cosy and safe as her own. |
| analysis of a video: Video Analysis and Repackaging for Distance Education A. Ranjith Ram, Subhasis Chaudhuri, 2012-06-13 This book presents various video processing methodologies that are useful for distance education. The motivation is to devise new multimedia technologies that are suitable for better representation of instructional videos by exploiting the temporal redundancies present in the original video. This solves many of the issues related to the memory and bandwidth limitation of lecture videos. The various methods described in the book focus on a key-frame based approach which is used to time shrink, repackage and retarget instructional videos. All the methods need a preprocessing step of shot detection and recognition, which is separately given as a chapter. We find those frames which are well-written and distinct as key-frames. A super-resolution based image enhancement scheme is suggested for refining the key-frames for better legibility. These key-frames, along with the audio and a meta-data for the mutual linkage among various media components form a repackaged lecture video, which on a programmed playback, render an estimate of the original video but at a substantially compressed form. The book also presents a legibility retentive retargeting of this instructional media on mobile devices with limited display size. All these technologies contribute to the enhancement of the outreach of distance education programs. Distance education is now a big business with an annual turnover of over 10-12 billion dollars. We expect this to increase rapidly. Use of the proposed technology will help deliver educational videos to those who are less endowed in terms of network bandwidth availability and to those everywhere who are even on a move by delivering it effectively to mobile handsets (including PDAs). Thus, technology developers, practitioners, and content providers will find the material very useful. |
| analysis of a video: Intelligent Video Event Analysis and Understanding Jianguo Zhang, Ling Shao, Lei Zhang, Graeme A. Jones, 2011-02-02 With the vast development of Internet capacity and speed, as well as wide adop- tion of media technologies in people’s daily life, a large amount of videos have been surging, and need to be efficiently processed or organized based on interest. The human visual perception system could, without difficulty, interpret and r- ognize thousands of events in videos, despite high level of video object clutters, different types of scene context, variability of motion scales, appearance changes, occlusions and object interactions. For a computer vision system, it has been be very challenging to achieve automatic video event understanding for decades. Broadly speaking, those challenges include robust detection of events under - tion clutters, event interpretation under complex scenes, multi-level semantic event inference, putting events in context and multiple cameras, event inference from object interactions, etc. In recent years, steady progress has been made towards better models for video event categorisation and recognition, e. g. , from modelling events with bag of spatial temporal features to discovering event context, from detecting events using a single camera to inferring events through a distributed camera network, and from low-level event feature extraction and description to high-level semantic event classification and recognition. Nowadays, text based video retrieval is widely used by commercial search engines. However, it is still very difficult to retrieve or categorise a specific video segment based on their content in a real multimedia system or in surveillance applications. |
| analysis of a video: Video Content Analysis Using Multimodal Information Ying Li, C.C. Jay Kuo, 2013-04-17 Video Content Analysis Using Multimodal Information For Movie Content Extraction, Indexing and Representation is on content-based multimedia analysis, indexing, representation and applications with a focus on feature films. Presented are the state-of-art techniques in video content analysis domain, as well as many novel ideas and algorithms for movie content analysis based on the use of multimodal information. The authors employ multiple media cues such as audio, visual and face information to bridge the gap between low-level audiovisual features and high-level video semantics. Based on sophisticated audio and visual content processing such as video segmentation and audio classification, the original video is re-represented in the form of a set of semantic video scenes or events, where an event is further classified as a 2-speaker dialog, a multiple-speaker dialog, or a hybrid event. Moreover, desired speakers are simultaneously identified from the video stream based on either a supervised or an adaptive speaker identification scheme. All this information is then integrated together to build the video's ToC (table of content) as well as the index table. Finally, a video abstraction system, which can generate either a scene-based summary or an event-based skim, is presented by exploiting the knowledge of both video semantics and video production rules. This monograph will be of great interest to research scientists and graduate level students working in the area of content-based multimedia analysis, indexing, representation and applications as well s its related fields. |
| analysis of a video: Integrating Video into Pre-Service and In-Service Teacher Training Rossi, Pier Giuseppe, Fedeli, Laura, 2016-09-12 The utilization of media has proven to be a beneficial instructional method in learning environments. These tools are particularly useful for teacher training, promoting better reflection on current practices. Integrating Video into Pre-Service and In-Service Teaching Training provides a comprehensive overview on the application of class video recordings to encourage self-observation of personal teaching methods and improve everyday classroom habits. Highlighting concepts relating to professionalism, didactics, and technological techniques, this book is a pivotal reference source for researchers, educators, practitioners, and students |
| analysis of a video: Video Analysis of Authentic Teaching Carrie Eunyoung Hong, Irene Van Riper, 2019-03-20 This book focuses on the use of teaching video for teachers’ professional development and growth. The objectives of the book are to discuss the benefits of video analysis of authentic teaching to improve teacher instruction in various contexts, such as advanced teacher education programs, in-service teacher professional development activities, teacher evaluation, and so on. This book reviews a theoretical framework and instructional strategies for video analysis among professional learning communities and provides research-based strategies to support video analysis of authentic teaching for teachers’ professional development activities. This book will benefit teacher educators who teach in-service teachers, school administrators who evaluate in-service teachers, and classroom teachers or supervisors who would like to reflect on their instructional practice and improve the learning of their students. It will serve as a resource for teacher educators and teachers of all subjects. |
| analysis of a video: Observing Teacher Identities through Video Analysis Amy Vetter, Melissa Schieble, 2015-09-25 Teaching is often seen as an identity process, with teachers constructing and enacting their identities through daily interactions with students, parents and colleagues. This volume explores how conducting video analysis helps teachers gain valuable perspectives on their own identities and improve classroom practice over time. This form of interactional awareness fosters reflection and action on creating classroom conditions that encourage equitable learning. The volume follows preservice English teachers as they examine video records of their practice during student teaching, and how the evidence impacts their development as literacy teachers of diverse adolescents. By applying an analytic framework to video analysis, the authors demonstrate how novice teachers use positioning theory to transform their own identity performance in the classroom. Education scholars, teachers and professional developers will greatly benefit from this unique perspective on teacher identity work. |
| analysis of a video: OPTICAL FLOW ANALYSIS AND MOTION ESTIMATION IN DIGITAL VIDEO WITH PYTHON AND TKINTER Vivian Siahaan, Rismon Hasiholan Sianipar, 2024-04-11 The first project, the GUI motion analysis tool gui_motion_analysis_fsbm.py, employs the Full Search Block Matching (FSBM) algorithm to analyze motion in videos. It imports essential libraries like tkinter, PIL, imageio, cv2, and numpy for GUI creation, image manipulation, video reading, computer vision tasks, and numerical computations. The script organizes its functionalities within the VideoFSBMOpticalFlow class, managing GUI elements through methods like create_widgets() for layout management, open_video() for video selection, and toggle_play_pause() for video playback control. It employs the FSBM algorithm for optical flow estimation, utilizing methods like full_search_block_matching() for motion vector calculation and show_optical_flow() for displaying motion patterns. Ultimately, by combining user-friendly controls with powerful analytical capabilities, the script facilitates efficient motion analysis in videos. The second project gui_motion_analysis_fsbm_dsa.py aims to provide a comprehensive solution for optical flow analysis through a user-friendly graphical interface. Leveraging the Full Search Block Matching (FSBM) algorithm with the Diamond Search Algorithm (DSA) optimization, it enables users to estimate motion patterns within video sequences efficiently. By integrating these algorithms into a GUI environment built with Tkinter, the script facilitates intuitive exploration and analysis of motion dynamics in various applications such as object tracking, video compression, and robotics. Key features include video file input, playback control, parameter adjustment, zooming capabilities, and optical flow visualization. Users can interactively analyze videos frame by frame, adjust algorithm parameters to tailor performance, and zoom in on specific regions of interest for detailed examination. Error handling mechanisms ensure robustness, while support for multiple instances enables simultaneous analysis of multiple videos. In essence, the project empowers users to gain insights into motion behaviors within video content, enhancing their ability to make informed decisions in diverse fields reliant on optical flow analysis. The third project Optical Flow Analysis with Three-Step Search (TSS) is dedicated to offering a user-friendly graphical interface for motion analysis in video sequences through the application of the Three-Step Search (TSS) algorithm. Optical flow analysis, pivotal in computer vision, facilitates tasks like video surveillance and object tracking. The implementation of TSS within the GUI environment allows users to efficiently estimate motion, empowering them with tools for detailed exploration and understanding of motion dynamics. Through its intuitive graphical interface, the project enables users to interactively engage with video content, from opening and previewing video files to controlling playback and navigating frames. Furthermore, it facilitates parameter customization, allowing users to fine-tune settings such as zoom scale and block size for tailored optical flow analysis. By overlaying visualizations of motion vectors on video frames, users gain insights into motion patterns, fostering deeper comprehension and analysis. Additionally, the project promotes community collaboration, serving as an educational resource and a platform for benchmarking different optical flow algorithms, ultimately advancing the field of computer vision technology. The fourth project gui_motion_analysis_bgds.py is developed with the primary objective of providing a user-friendly graphical interface (GUI) application for analyzing optical flow within video sequences, utilizing the Block-based Gradient Descent Search (BGDS) algorithm. Its purpose is to facilitate comprehensive exploration and understanding of motion patterns in video data, catering to diverse domains such as computer vision, video surveillance, and human-computer interaction. By offering intuitive controls and interactive functionalities, the application empowers users to delve into the intricacies of motion dynamics, aiding in research, education, and practical applications. Through the GUI interface, users can seamlessly open and analyze video files, spanning formats like MP4, AVI, or MKV, thus enabling thorough examination of motion behaviors within different contexts. The application supports essential features such as video playback control, zoom adjustment, frame navigation, and parameter customization. Leveraging the BGDS algorithm, motion vectors are computed at the block level, furnishing users with detailed insights into motion characteristics across successive frames. Additionally, the GUI facilitates real-time visualization of computed optical flow fields alongside original video frames, enhancing users' ability to interpret and analyze motion information effectively. With support for multiple instances and configurable parameters, the application caters to a broad spectrum of users, serving as a versatile tool for motion analysis endeavors in various professional and academic endeavors. The fifth project gui_motion_analysis_hbm2.py serves as a comprehensive graphical user interface (GUI) application tailored for optical flow analysis in video files. Leveraging the Tkinter library, it provides a user-friendly platform for scrutinizing the apparent motion of objects between consecutive frames, essential for various applications like object tracking and video compression. The algorithm of choice for optical flow analysis is the Hierarchical Block Matching (HBM) technique enhanced with the Three-Step Search (TSS) optimization, renowned for its effectiveness in motion estimation tasks. Primarily, the GUI layout encompasses a video display panel alongside control buttons facilitating actions such as video file opening, playback control, frame navigation, and parameter specification for optical flow analysis. Users can seamlessly open supported video files (e.g., MP4, AVI, MKV) and adjust parameters like zoom scale, step size, block size, and search range to tailor the analysis according to their needs. Through interactive features like zooming, panning, and dragging to manipulate the optical flow visualization, users gain insights into motion patterns with ease. Furthermore, the application supports additional functionalities such as time-based navigation, parallel analysis through multiple instances, ensuring a versatile and user-centric approach to optical flow analysis. The sixth project object_tracking_fsbm.py is designed to showcase object tracking capabilities using the Full Search Block Matching Algorithm (FSBM) within a user-friendly graphical interface (GUI) developed with Tkinter. By integrating this algorithm with a robust GUI, the project aims to offer a practical demonstration of object tracking techniques commonly utilized in computer vision applications. Upon execution, the script initializes a Tkinter window and sets up essential widgets for video display, playback control, and parameter adjustment. Users can seamlessly open video files in various formats and navigate through frames with intuitive controls, facilitating efficient analysis and tracking of objects. Leveraging the FSBM algorithm, object tracking is achieved by comparing pixel blocks between consecutive frames to estimate motion vectors, enabling real-time visualization of object movements within the video stream. The GUI provides interactive features like bounding box initialization, parameter adjustment, and zoom functionality, empowering users to fine-tune the tracking process and analyze objects with precision. Overall, the project serves as a comprehensive platform for object tracking, combining algorithmic prowess with an intuitive interface for effective analysis and visualization of object motion in video streams. The seventh project showcases an object tracking application seamlessly integrated with a graphical user interface (GUI) developed using Tkinter. Users can effortlessly interact with video files of various formats (MP4, AVI, MKV, WMV) through intuitive controls such as play, pause, and stop for video playback, as well as frame-by-frame navigation. The GUI further enhances user experience by providing zoom functionality for detailed examination of video content, contributing to a comprehensive and user-friendly environment. Central to the application is the implementation of the Diamond Search Algorithm (DSA) for object tracking, enabling the calculation of motion vectors between consecutive frames. These motion vectors facilitate the dynamic adjustment of a bounding box around the tracked object, offering visual feedback to users. Leveraging event handling mechanisms like mouse wheel scrolling and button press-and-drag, along with error handling for smooth operation, the project demonstrates the practical fusion of computer vision techniques with GUI development, exemplifying the real-world application of algorithms like DSA in object tracking scenarios. The eight project aims to provide an interactive graphical user interface (GUI) application for object tracking, employing the Three-Step Search (TSS) algorithm for motion estimation. The ObjectTrackingFSBM_TSS class defines the GUI layout, featuring essential widgets for video display, control buttons, and parameter inputs for block size and search range. Users can effortlessly interact with the application, from opening video files to controlling video playback and adjusting tracking parameters, facilitating seamless exploration of object motion within video sequences. Central to the application's functionality are the full_search_block_matching_tss() and track_object() methods, responsible for implementing the TSS algorithm and object tracking process, respectively. The full_search_block_matching_tss() method iterates over blocks in consecutive frames, utilizing TSS to calculate motion vectors. These vectors are then used in the track_object() method to update the bounding box around the object of interest, enabling real-time tracking. The GUI dynamically displays video frames and updates the bounding box position, providing users with a comprehensive tool for interactive object tracking and motion analysis. The ninth project encapsulates an object tracking application utilizing the Block-based Gradient Descent Search (BGDS) algorithm, providing users with a user-friendly interface developed using the Tkinter library for GUI and OpenCV for video processing. Upon initialization, the class orchestrates the setup of GUI components, offering intuitive controls for video manipulation and parameter configuration to enhance the object tracking process. Users can seamlessly open video files, control video playback, and adjust algorithm parameters such as block size, search range, iteration limit, and learning rate, empowering them with comprehensive tools for efficient motion estimation. The application's core functionality lies in the block_based_gradient_descent_search() method, implementing the BGDS algorithm for motion estimation by iteratively optimizing motion vectors over blocks in consecutive frames. Leveraging these vectors, the track_object() method dynamically tracks objects within a bounding box, computing mean motion vectors to update bounding box coordinates in real-time. Additionally, interactive features enable users to define bounding boxes around objects of interest through mouse events, facilitating seamless object tracking visualization. Overall, the ObjectTracking_BGDS class offers a versatile and user-friendly platform for object tracking, showcasing the practical application of the BGDS algorithm in real-world scenarios with enhanced ease of use and efficiency. |
| analysis of a video: Experiments and Video Analysis in Classical Mechanics Vitor L. B. de Jesus, 2017-03-24 This book is an experimental physics textbook on classical mechanics focusing on the development of experimental skills by means of discussion of different aspects of the experimental setup and the assessment of common issues such as accuracy and graphical representation. The most important topics of an experimental physics course on mechanics are covered and the main concepts are explored in detail. Each chapter didactically connects the experiment and the theoretical models available to explain it. Real data from the proposed experiments are presented and a clear discussion over the theoretical models is given. Special attention is also dedicated to the experimental uncertainty of measurements and graphical representation of the results. In many of the experiments, the application of video analysis is proposed and compared with traditional methods. |
| analysis of a video: FRAME ANALYSIS AND PROCESSING IN DIGITAL VIDEO USING PYTHON AND TKINTER Vivian Siahaan, Rismon Hasiholan Sianipar, 2024-03-27 The first project in chapter one which is Canny Edge Detector presented here is a graphical user interface (GUI) application built using Tkinter in Python. This application allows users to open video files (of formats like mp4, avi, or mkv) and view them along with their corresponding Canny edge detection frames. The application provides functionalities such as playing, pausing, stopping, navigating through frames, and jumping to specific times within the video. Upon opening the application, users are greeted with a clean interface comprising two main sections: the video display panel and the control panel. The video display panel consists of two canvas widgets, one for displaying the original video and another for displaying the Canny edge detection result. These canvases allow users to visualize the video and its corresponding edge detection in real-time. The control panel houses various buttons and widgets for controlling the video playback and interaction. Users can open video files using the Open Video button, select a zoom scale for viewing convenience, jump to specific times within the video, play/pause the video, stop the video, navigate through frames, and even open another instance of the application for simultaneous use. The core functionality lies in the methods responsible for displaying frames and performing Canny edge detection. The show_frame() method retrieves frames from the video, resizes them based on the selected zoom scale, and displays them on the original video canvas. Similarly, the show_canny_frame() method applies the Canny edge detection algorithm to the frames, enhances the edges using dilation, and displays the resulting edge detection frames on the corresponding canvas. The application also supports mouse interactions such as dragging to pan the video frames within the canvas and scrolling to navigate through frames. These interactions are facilitated by event handling methods like on_press(), on_drag(), and on_scroll(), ensuring smooth user experience and intuitive control over video playback and exploration. Overall, this project provides a user-friendly platform for visualizing video content and exploring Canny edge detection results, making it valuable for educational purposes, research, or practical applications involving image processing and computer vision. This second project in chapter one implements a graphical user interface (GUI) application for performing edge detection using the Prewitt operator on videos. The purpose of the code is to provide users with a tool to visualize videos, apply the Prewitt edge detection algorithm, and interactively control playback and visualization parameters. The third project in chapter one which is Sobel Edge Detector is implemented in Python using Tkinter and OpenCV serves as a graphical user interface (GUI) for viewing and analyzing videos with real-time Sobel edge detection capabilities. The Frei-Chen Edge Detection project as fourth project in chapter one is a graphical user interface (GUI) application built using Python and the Tkinter library. The application is designed to process and visualize video files by detecting edges using the Frei-Chen edge detection algorithm. The core functionality of the application lies in the implementation of the Frei-Chen edge detection algorithm. This algorithm involves convolving the video frames with predefined kernels to compute the gradient magnitude, which represents the strength of edges in the image. The resulting edge-detected frames are thresholded to convert grayscale values to binary values, enhancing the visibility of edges. The application also includes features for user interaction, such as mouse wheel scrolling to zoom in and out, click-and-drag functionality to pan across the video frames, and input fields for jumping to specific times within the video. Additionally, users have the option to open multiple instances of the application simultaneously to analyze different videos concurrently, providing flexibility and convenience in video processing tasks. Overall, the Frei-Chen Edge Detection project offers a user-friendly interface for edge detection in videos, empowering users to explore and analyze visual data effectively. The KIRSCH EDGE DETECTOR project as the fifth project in chapter one is a Python application built using Tkinter, OpenCV, and NumPy libraries for performing edge detection on video files. It handles the visualization of the edge-detected frames in real-time. It retrieves the current frame from the video, applies Gaussian blur for noise reduction, performs Kirsch edge detection, and applies thresholding to obtain the binary edge image. The processed frame is then displayed on the canvas alongside the original video. This SCHARR EDGE DETECTOR as the sixth project in chapter one is creating a graphical user interface (GUI) to visualize edge detection in videos using the Scharr algorithm. It allows users to open video files, play/pause video playback, navigate frame by frame, and apply Scharr edge detection in real-time. The GUI consists of multiple components organized into panels. The main panel displays the original video on the left side and the edge-detected video using the Scharr algorithm on the right side. Both panels utilize Tkinter Canvas widgets for efficient rendering and manipulation of video frames. Users can interact with the application using control buttons located in the control panel. These buttons include options to open a video file, adjust the zoom scale, jump to a specific time in the video, play/pause video playback, stop the video, navigate to the previous or next frame, and open another instance of the application for parallel video analysis. The core functionality of the application lies in the VideoScharr class, which encapsulates methods for video loading, playback control, frame processing, and edge detection using the Scharr algorithm. The apply_scharr method implements the Scharr edge detection algorithm, applying a pair of 3x3 convolution kernels to compute horizontal and vertical derivatives of the image and then combining them to calculate the edge magnitude. Overall, the SCHARR EDGE DETECTOR project provides users with an intuitive interface to explore edge detection techniques in videos using the Scharr algorithm. It combines the power of image processing libraries like OpenCV and the flexibility of Tkinter for creating interactive and responsive GUI applications in Python. The first project in chapter two is designed to provide a user-friendly interface for processing video frames using Gaussian filtering techniques. It encompasses various components and functionalities tailored towards efficient video analysis and processing. The GaussianFilter Class serves as the backbone of the application, managing GUI initialization and video processing functionalities. The GUI layout is constructed with Tkinter widgets, comprising two main panels for video display and control buttons. Key functionalities include opening video files, controlling playback, adjusting zoom levels, navigating frames, and interacting with video frames via mouse events. Additionally, users can process frames using OpenCV for Gaussian filtering to enhance video quality and reduce noise. Time navigation functionality allows users to jump to specific time points in the video. Moreover, the application supports multiple instances for simultaneous video analysis in independent windows. Overall, this project offers a comprehensive toolset for video analysis and processing, empowering users with an intuitive interface and diverse functionalities. The second project in chapter two presents a Tkinter application tailored for video frame filtering utilizing a mean filter. It offers comprehensive functionalities including opening, playing/pausing, and stopping video playback, alongside options to navigate to previous and next frames, jump to specified times, and adjust zoom scale. Displayed on separate canvases, the original and filtered video frames are showcased distinctly. Upon video file opening, the application utilizes imageio.get_reader() for video reading, while play_video() and play_filtered_video() methods handle frame display. Individual frame rendering is managed by show_frame() and show_mean_frame(), incorporating noise addition through the add_noise() method. Mouse wheel scrolling, canvas dragging, and scrollbar scrolling are facilitated through event handlers, enhancing user interaction. Supplementary functionalities include time navigation, frame navigation, and the ability to open multiple instances using open_another_player(). The main() function initializes the Tkinter application and executes the event loop for GUI display. The third project in chapter two aims to develop a user-friendly graphical interface application for filtering video frames with a median filter. Supporting various video formats like MP4, AVI, and MKV, users can seamlessly open, play, pause, stop, and navigate through video frames. The key feature lies in real-time application of the median filter to enhance frame quality by noise reduction. Upon video file opening, the original frames are displayed alongside filtered frames, with users empowered to control zoom levels and frame navigation. Leveraging libraries such as tkinter, imageio, PIL, and OpenCV, the application facilitates efficient video analysis and processing, catering to diverse domains like surveillance, medical imaging, and scientific research. The fourth project in chapter two exemplifies the utilization of a bilateral filter within a Tkinter-based graphical user interface (GUI) for real-time video frame filtering. The script showcases the application of bilateral filtering, renowned for its ability to smooth images while preserving edges, to enhance video frames. The GUI integrates two main components: canvas panels for displaying original and filtered frames, facilitating interactive viewing and manipulation. Upon video file opening, original frames are displayed on the left panel, while bilateral-filtered frames appear on the right. Adjustable parameters within the bilateral filter method enable fine-tuning for noise reduction and edge preservation based on specific video characteristics. Control functionalities for playback, frame navigation, zoom scaling, and time jumping enhance user interaction, providing flexibility in exploring diverse video filtering techniques. Overall, the script offers a practical demonstration of bilateral filtering in real-time video processing within a Tkinter GUI, enabling efficient exploration of filtering methodologies. The fifth project in chapter two integrates a video player application with non-local means denoising functionality, utilizing tkinter for GUI design, PIL for image processing, imageio for video file reading, and OpenCV for denoising. The GUI, set up by the NonLocalMeansDenoising class, includes controls for playback, zoom, time navigation, and frame browsing, alongside features like mouse wheel scrolling and dragging for user interaction. Video loading and display are managed through methods like open_video and play_video(), which iterate through frames, resize them, and add noise for display on the canvas. Non-local means denoising is applied using the apply_non_local_denoising() method, enhancing frames before display on the filter canvas via show_non_local_frame(). The GUI fosters user interaction, offering controls for playback, zoom, time navigation, and frame browsing, while also ensuring error handling for seamless operation during video loading, processing, and denoising. The sixth project in chapter two provides a platform for filtering video frames using anisotropic diffusion. Users can load various video formats and control playback (play, pause, stop) while adjusting zoom levels and jumping to specific timestamps. Original video frames are displayed alongside filtered versions achieved through anisotropic diffusion, aiming to denoise images while preserving critical edges and structures. Leveraging OpenCV and imageio for image processing and PIL for manipulation tasks, the application offers a user-friendly interface with intuitive control buttons and multi-video instance support, facilitating efficient analysis and enhancement of video content through anisotropic diffusion-based filtering. The seventh project in chapter two is built with Tkinter and OpenCV for filtering video frames using the Wiener filter. It offers a user-friendly interface for opening video files, controlling playback, adjusting zoom levels, and applying the Wiener filter for noise reduction. With separate panels for displaying original and filtered video frames, users can interact with the frames via zooming, scrolling, and dragging functionalities. The application handles video processing internally by adding random noise to frames and applying the Wiener filter, ensuring enhanced visual quality. Overall, it provides a convenient tool for visualizing and analyzing videos while showcasing the effectiveness of the Wiener filter in image processing tasks. The first project in chapter three showcases optical flow observation using the Lucas-Kanade method. Users can open video files, play, pause, and stop them, adjust zoom levels, and jump to specific frames. The interface comprises two panels for original video display and optical flow results. With functionalities like frame navigation, zoom adjustment, and time-based jumping, users can efficiently analyze optical flow patterns. The Lucas-Kanade algorithm computes optical flow between consecutive frames, visualized as arrows and points, allowing users to observe directional changes and flow strength. Mouse wheel scrolling facilitates zoom adjustments for detailed inspection or broader perspective viewing. Overall, the application provides intuitive navigation and robust optical flow analysis tools for effective video observation. The second project in chapter three is designed to visualize optical flow with Kalman filtering. It features controls for video file manipulation, frame navigation, zoom adjustment, and parameter specification. The application provides side-by-side canvases for displaying original video frames and optical flow results, allowing users to interact with the frames and explore flow patterns. Internally, it employs OpenCV and NumPy for optical flow computation using the Farneback method, enhancing stability and accuracy with Kalman filtering. Overall, it offers a user-friendly interface for analyzing video data, benefiting fields like computer vision and motion tracking. The third project in chapter three is for optical flow analysis in videos using Gaussian pyramid techniques. Users can open video files and visualize optical flow between consecutive frames. The interface presents two panels: one for original video frames and the other for computed optical flow. Users can adjust zoom levels and specify optical flow parameters. Control buttons enable common video playback actions, and multiple instances can be opened for simultaneous analysis. Internally, OpenCV, Tkinter, and imageio libraries are used for video processing, GUI development, and image manipulation, respectively. Optical flow computation relies on the Farneback method, with resulting vectors visualized on the frames to reveal motion patterns. |
| analysis of a video: Key Frame Extraction of Surveillance Video Based on Motion Analysis Yunzuo Zhang, Shasha Zhang, 2021-01-29 Surveillance video has a wide range of applications in many fields, such as national economic construction, public security information construction, and widely adopted countries worldwide. The infrastructure of the video surveillance system has begun to take shape and is still rapid expansion. As thousands of surveillance cameras monitor and record round the clock, the amount of video data has been explosive growth. Finding the required information in a large number of surveillance videos is undoubtedly a needle in a haystack. Motion is a significant feature of video, and much meaningful visual information is contained in the movement. In many application scenarios, like road traffic monitoring, security for major events, guidance for military aircraft, and autonomous vehicle, people tend to pay more attention to the moving object. The analysis shows that the key frame extraction of surveillance video based on motion analysis has important practical significance. Hence, this book puts forward several key frame extraction methods of surveillance video around capturing the target motion state. |
| analysis of a video: Video Modelling and Behaviour Analysis Christos Nikopoulos, Michael Keenan, 2006 Video modelling is a behaviour modification technique using videotaped scenarios for the child to observe, concentrating the focus of attention and creating an effective stimulus for learning. This book introduces the technique. Illustrative case examples are supported by detailed diagrams and photographs, with clear, accessible explanations. |
| analysis of a video: Bridging the Semantic Gap in Image and Video Analysis Halina Kwaśnicka, Lakhmi C. Jain, 2018-02-20 This book presents cutting-edge research on various ways to bridge the semantic gap in image and video analysis. The respective chapters address different stages of image processing, revealing that the first step is a future extraction, the second is a segmentation process, the third is object recognition, and the fourth and last involve the semantic interpretation of the image. The semantic gap is a challenging area of research, and describes the difference between low-level features extracted from the image and the high-level semantic meanings that people can derive from the image. The result greatly depends on lower level vision techniques, such as feature selection, segmentation, object recognition, and so on. The use of deep models has freed humans from manually selecting and extracting the set of features. Deep learning does this automatically, developing more abstract features at the successive levels. The book offers a valuable resource for researchers, practitioners, students and professors in Computer Engineering, Computer Science and related fields whose work involves images, video analysis, image interpretation and so on. |
| analysis of a video: Digital Video Concepts, Methods, and Metrics Shahriar Akramullah, 2014-11-05 Digital Video Concepts, Methods, and Metrics: Quality, Compression, Performance, and Power Trade-off Analysis is a concise reference for professionals in a wide range of applications and vocations. It focuses on giving the reader mastery over the concepts, methods and metrics of digital video coding, so that readers have sufficient understanding to choose and tune coding parameters for optimum results that would suit their particular needs for quality, compression, speed and power. The practical aspects are many: Uploading video to the Internet is only the beginning of a trend where a consumer controls video quality and speed by trading off various other factors. Open source and proprietary applications such as video e-mail, private party content generation, editing and archiving, and cloud asset management would give further control to the end-user. Digital video is frequently compressed and coded for easier storage and transmission. This process involves visual quality loss due to typical data compression techniques and requires use of high performance computing systems. A careful balance between the amount of compression, the visual quality loss and the coding speed is necessary to keep the total system cost down, while delivering a good user experience for various video applications. At the same time, power consumption optimizations are also essential to get the job done on inexpensive consumer platforms. Trade-offs can be made among these factors, and relevant considerations are particularly important in resource-constrained low power devices. To better understand the trade-offs this book discusses a comprehensive set of engineering principles, strategies, methods and metrics. It also exposes readers to approaches on how to differentiate and rank video coding solutions. |
| analysis of a video: Design of Video Quality Metrics with Multi-Way Data Analysis Christian Keimel, 2015-12-29 This book proposes a data-driven methodology using multi-way data analysis for the design of video-quality metrics. It also enables video- quality metrics to be created using arbitrary features. This data- driven design approach not only requires no detailed knowledge of the human visual system, but also allows a proper consideration of the temporal nature of video using a three-way prediction model, corresponding to the three-way structure of video. Using two simple example metrics, the author demonstrates not only that this purely data- driven approach outperforms state-of-the-art video-quality metrics, which are often optimized for specific properties of the human visual system, but also that multi-way data analysis methods outperform the combination of two-way data analysis methods and temporal pooling. |
| analysis of a video: Odyssey Homer, 2018-10-23 This work has been selected by scholars as being culturally important and is part of the knowledge base of civilization as we know it. This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. To ensure a quality reading experience, this work has been proofread and republished using a format that seamlessly blends the original graphical elements with text in an easy-to-read typeface. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant. |
| analysis of a video: Bridging the Gap Between AI, Cognitive Science, and Narratology With Narrative Generation Ogata, Takashi, Ono, Jumpei, 2020-09-25 The use of cognitive science in creating stories, languages, visuals, and characters is known as narrative generation, and it has become a trending area of study. Applying artificial intelligence (AI) techniques to story development has caught the attention of professionals and researchers; however, few studies have inherited techniques used in previous literary methods and related research in social sciences. Implementing previous narratology theories to current narrative generation systems is a research area that remains unexplored. Bridging the Gap Between AI, Cognitive Science, and Narratology With Narrative Generation is a collection of innovative research on the analysis of current practices in narrative generation systems by combining previous theories in narratology and literature with current methods of AI. The book bridges the gap between AI, cognitive science, and narratology with narrative generation in a broad sense, including other content generation, such as a novels, poems, movies, computer games, and advertisements. The book emphasizes that an important method for bridging the gap is based on designing and implementing computer programs using knowledge and methods of narratology and literary theories. In order to present an organic, systematic, and integrated combination of both the fields to develop a new research area, namely post-narratology, this book has an important place in the creation of a new research area and has an impact on both narrative generation studies, including AI and cognitive science, and narrative studies, including narratology and literary theories. It is ideally designed for academicians, researchers, and students, as well as enterprise practitioners, engineers, and creators of diverse content generation fields such as advertising production, computer game creation, comic and manga writing, and movie production. |
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