Video Object Extraction and Representation
Video Object Extraction and Representation

Author: I-Jong Lin

Publisher: Springer Science & Business Media

Published: 2005-11-30

Total Pages: 184

ISBN-13: 0306470373

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“If you have built castles in the air, your work need not be lost; that is where they should be. Now put the foundations under them. ” - Henry David Thoreau, Walden Although engineering is a study entrenched firmly in belief of pr- matism, I have always believed its impact need not be limited to pr- matism. Pragmatism is not the boundaries that define engineering, just the (sometimes unforgiving) rules by which we sight our goals. This book studies two major problems of content-based video proce- ing for a media-based technology: Video Object Plane (VOP) Extr- tion and Representation, in support of the MPEG-4 and MPEG-7 video standards, respectively. After reviewing relevant image and video p- cessing techniques, we introduce the concept of Voronoi Ordered Spaces for both VOP extraction and representation to integrate shape infor- tion into low-level optimization algorithms and to derive robust shape descriptors, respectively. We implement a video object segmentation system with a novel surface optimization scheme that integrates Voronoi Ordered Spaces with existing techniques to balance visual information against predictions of models of a priori information. With these VOPs, we have explicit forms of video objects that give users the ability to - dress and manipulate video content. We outline a general methodology of robust data representation and comparison through the concept of complex partitioning mapped onto Directed Acyclic Graphs (DAGs).

Feature Extraction and Image Processing for Computer Vision
Feature Extraction and Image Processing for Computer Vision

Author: Mark Nixon

Publisher: Academic Press

Published: 2012-12-18

Total Pages: 629

ISBN-13: 0123978246

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Feature Extraction and Image Processing for Computer Vision is an essential guide to the implementation of image processing and computer vision techniques, with tutorial introductions and sample code in Matlab. Algorithms are presented and fully explained to enable complete understanding of the methods and techniques demonstrated. As one reviewer noted, "The main strength of the proposed book is the exemplar code of the algorithms." Fully updated with the latest developments in feature extraction, including expanded tutorials and new techniques, this new edition contains extensive new material on Haar wavelets, Viola-Jones, bilateral filtering, SURF, PCA-SIFT, moving object detection and tracking, development of symmetry operators, LBP texture analysis, Adaboost, and a new appendix on color models. Coverage of distance measures, feature detectors, wavelets, level sets and texture tutorials has been extended. - Named a 2012 Notable Computer Book for Computing Methodologies by Computing Reviews - Essential reading for engineers and students working in this cutting-edge field - Ideal module text and background reference for courses in image processing and computer vision - The only currently available text to concentrate on feature extraction with working implementation and worked through derivation

Practical Machine Learning for Computer Vision
Practical Machine Learning for Computer Vision

Author: Valliappa Lakshmanan

Publisher: "O'Reilly Media, Inc."

Published: 2021-07-21

Total Pages: 481

ISBN-13: 1098102339

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This practical book shows you how to employ machine learning models to extract information from images. ML engineers and data scientists will learn how to solve a variety of image problems including classification, object detection, autoencoders, image generation, counting, and captioning with proven ML techniques. This book provides a great introduction to end-to-end deep learning: dataset creation, data preprocessing, model design, model training, evaluation, deployment, and interpretability. Google engineers Valliappa Lakshmanan, Martin Görner, and Ryan Gillard show you how to develop accurate and explainable computer vision ML models and put them into large-scale production using robust ML architecture in a flexible and maintainable way. You'll learn how to design, train, evaluate, and predict with models written in TensorFlow or Keras. You'll learn how to: Design ML architecture for computer vision tasks Select a model (such as ResNet, SqueezeNet, or EfficientNet) appropriate to your task Create an end-to-end ML pipeline to train, evaluate, deploy, and explain your model Preprocess images for data augmentation and to support learnability Incorporate explainability and responsible AI best practices Deploy image models as web services or on edge devices Monitor and manage ML models

Management of Multimedia on the Internet
Management of Multimedia on the Internet

Author: Ehab S. Al-Shaer

Publisher: Springer Science & Business Media

Published: 2001-10-10

Total Pages: 387

ISBN-13: 3540427864

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In recent years we have witnessed the explosion of multimedia traffic on the Internet. The availability of high bandwidth connections together with the recent advances in high quality video and audio compression techniques have created a fertile ground for the growth of multimedia applications such as interactive video on demand, collaborative distance learning, and remote medical diagnosis. Furthermore, the availability of low bit rate video and audio applications (e.g., H.263 and G.728) and the proliferation of pervasive devices create a new demand for wireless multimedia communication systems. After a decade or more of research and development in multimedia networking, the research community has learned a number of lessons. First, increasing the capacity of the “best effort” networks and services does not provide an effective and permanent solution for offering a guaranteed Quality of Service (QoS). Second, the integration of service and network management is a key element in providing end to end service management. Third, management techniques for Internet multimedia services must be scalable and adaptive to guarantee QoS and maintain fairness with optimal network resource.

Multisensor Surveillance Systems
Multisensor Surveillance Systems

Author: Gian Luca Foresti

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 283

ISBN-13: 146150371X

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Monitoring of public and private sites is increasingly becoming a very important and critical issue, especially after the recent flurry of terrorist attacks including the one on the Word Trade Center in September 2001. It is, therefore, imperative that effective multisensor surveillance systems be developed to protect the society from similar attacks in the future. The new generation of surveillance systems to be developed have a specific requirement: they must be able to automatically identify criminal and terrorist activity without sacrificing individual privacy to the extent possible. Privacy laws concerning monitoring and surveillance systems vary from country to country but, in general, they try to protect the privacy of their citizens. Monitoring and visual surveillance has numerous other applications. It can be employed to help invalids or handicapped and to monitor the activities of elderly people. It can be used to monitor large events such as sporting events, as well. Nowadays, monitoring is employ~d in several different contexts including transport applications, such as monitoring of railway stations and airports, dangerous environments like nuclear facilities or traffic flows on roads and bridges. The latest generation of surveillance systems mainly rely on hybrid analog-digital, or completely digital video communications and processing methods and take advantage of the greater of flexibility offered by video processing algorithms that are capable focusing a human operator's attention on a set of interesting situations.

Content-Based Video Retrieval
Content-Based Video Retrieval

Author: Milan Petkovic

Publisher: Springer Science & Business Media

Published: 2013-06-29

Total Pages: 157

ISBN-13: 1475748655

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The area of content-based video retrieval is a very hot area both for research and for commercial applications. In order to design effective video databases for applications such as digital libraries, video production, and a variety of Internet applications, there is a great need to develop effective techniques for content-based video retrieval. One of the main issues in this area of research is how to bridge the semantic gap between low-Ievel features extracted from a video (such as color, texture, shape, motion, and others) and semantics that describe video concept on a higher level. In this book, Dr. Milan Petkovi6 and Prof. Dr. Willem Jonker have addressed this issue by developing and describing several innovative techniques to bridge the semantic gap. The main contribution of their research, which is the core of the book, is the development of three techniques for bridging the semantic gap: (1) a technique that uses the spatio-temporal extension of the Cobra framework, (2) a technique based on hidden Markov models, and (3) a technique based on Bayesian belief networks. To evaluate performance of these techniques, the authors have conducted a number of experiments using real video data. The book also discusses domains solutions versus general solution of the problem. Petkovi6 and Jonker proposed a solution that allows a system to be applied in multiple domains with minimal adjustments. They also designed and described a prototype video database management system, which is based on techniques they proposed in the book.

Multimedia Systems, Standards, and Networks
Multimedia Systems, Standards, and Networks

Author: Atul Puri

Publisher: CRC Press

Published: 2000-03-22

Total Pages: 653

ISBN-13: 0824741560

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Describes ITU H H.323 and H.324, H.263, ITU-T video, and MPEG-4 standards, systems, and coding; IP and ATM networks; multimedia search and retrieval; image retrieval in digital laboratories; and the status and direction of MPEG-7.

Adaptive Multimedia Retrieval:User, Context, and Feedback
Adaptive Multimedia Retrieval:User, Context, and Feedback

Author: Stéphane Marchand-Maillet

Publisher: Springer

Published: 2007-06-20

Total Pages: 279

ISBN-13: 3540715452

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This book constitutes the thoroughly refereed post-proceedings of the 4th International Workshop on Adaptive Multimedia Retrieval, AMR 2006, held in Geneva, Switzerland in July 2006. The papers cover ontology-based retrieval and annotation, ranking and similarity measurements, music information retrieval, visual modeling, adaptive retrieval, structuring multimedia, as well as user integration and profiling.

Representations and Techniques for 3D Object Recognition and Scene Interpretation
Representations and Techniques for 3D Object Recognition and Scene Interpretation

Author: Derek Hoiem

Publisher: Morgan & Claypool Publishers

Published: 2011

Total Pages: 172

ISBN-13: 1608457281

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One of the grand challenges of artificial intelligence is to enable computers to interpret 3D scenes and objects from imagery. This book organizes and introduces major concepts in 3D scene and object representation and inference from still images, with a focus on recent efforts to fuse models of geometry and perspective with statistical machine learning. The book is organized into three sections: (1) Interpretation of Physical Space; (2) Recognition of 3D Objects; and (3) Integrated 3D Scene Interpretation. The first discusses representations of spatial layout and techniques to interpret physical scenes from images. The second section introduces representations for 3D object categories that account for the intrinsically 3D nature of objects and provide robustness to change in viewpoints. The third section discusses strategies to unite inference of scene geometry and object pose and identity into a coherent scene interpretation. Each section broadly surveys important ideas from cognitive science and artificial intelligence research, organizes and discusses key concepts and techniques from recent work in computer vision, and describes a few sample approaches in detail. Newcomers to computer vision will benefit from introductions to basic concepts, such as single-view geometry and image classification, while experts and novices alike may find inspiration from the book's organization and discussion of the most recent ideas in 3D scene understanding and 3D object recognition. Specific topics include: mathematics of perspective geometry; visual elements of the physical scene, structural 3D scene representations; techniques and features for image and region categorization; historical perspective, computational models, and datasets and machine learning techniques for 3D object recognition; inferences of geometrical attributes of objects, such as size and pose; and probabilistic and feature-passing approaches for contextual reasoning about 3D objects and scenes. Table of Contents: Background on 3D Scene Models / Single-view Geometry / Modeling the Physical Scene / Categorizing Images and Regions / Examples of 3D Scene Interpretation / Background on 3D Recognition / Modeling 3D Objects / Recognizing and Understanding 3D Objects / Examples of 2D 1/2 Layout Models / Reasoning about Objects and Scenes / Cascades of Classifiers / Conclusion and Future Directions