Download Free 2007 Ieee Workshop On Motion And Video Computing Book in PDF and EPUB Free Download. You can read online 2007 Ieee Workshop On Motion And Video Computing and write the review.

Half a century into the digital era, the profound impact of information technology on intellectual and cultural life is universally acknowledged but still poorly understood. The sheer complexity of the technology coupled with the rapid pace of change makes it increasingly difficult to establish common ground and to promote thoughtful discussion. Responding to this challenge, Switching Codes brings together leading American and European scholars, scientists, and artists—including Charles Bernstein, Ian Foster, Bruno Latour, Alan Liu, and Richard Powers—to consider how the precipitous growth of digital information and its associated technologies are transforming the ways we think and act. Employing a wide range of forms, including essay, dialogue, short fiction, and game design, this book aims to model and foster discussion between IT specialists, who typically have scant training in the humanities or traditional arts, and scholars and artists, who often understand little about the technologies that are so radically transforming their fields. Switching Codes will be an indispensable volume for anyone seeking to understand the impact of digital technology on contemporary culture, including scientists, educators, policymakers, and artists, alike.
This book addresses one of the most important unsolved problems in artificial intelligence: the task of learning, in an unsupervised manner, from massive quantities of spatiotemporal visual data that are available at low cost. The book covers important scientific discoveries and findings, with a focus on the latest advances in the field. Presenting a coherent structure, the book logically connects novel mathematical formulations and efficient computational solutions for a range of unsupervised learning tasks, including visual feature matching, learning and classification, object discovery, and semantic segmentation in video. The final part of the book proposes a general strategy for visual learning over several generations of student-teacher neural networks, along with a unique view on the future of unsupervised learning in real-world contexts. Offering a fresh approach to this difficult problem, several efficient, state-of-the-art unsupervised learning algorithms are reviewed in detail, complete with an analysis of their performance on various tasks, datasets, and experimental setups. By highlighting the interconnections between these methods, many seemingly diverse problems are elegantly brought together in a unified way. Serving as an invaluable guide to the computational tools and algorithms required to tackle the exciting challenges in the field, this book is a must-read for graduate students seeking a greater understanding of unsupervised learning, as well as researchers in computer vision, machine learning, robotics, and related disciplines.
Background modeling and foreground detection are important steps in video processing used to detect robustly moving objects in challenging environments. This requires effective methods for dealing with dynamic backgrounds and illumination changes as well as algorithms that must meet real-time and low memory requirements. Incorporating both established and new ideas, Background Modeling and Foreground Detection for Video Surveillance provides a complete overview of the concepts, algorithms, and applications related to background modeling and foreground detection. Leaders in the field address a wide range of challenges, including camera jitter and background subtraction. The book presents the top methods and algorithms for detecting moving objects in video surveillance. It covers statistical models, clustering models, neural networks, and fuzzy models. It also addresses sensors, hardware, and implementation issues and discusses the resources and datasets required for evaluating and comparing background subtraction algorithms. The datasets and codes used in the text, along with links to software demonstrations, are available on the book’s website. A one-stop resource on up-to-date models, algorithms, implementations, and benchmarking techniques, this book helps researchers and industry developers understand how to apply background models and foreground detection methods to video surveillance and related areas, such as optical motion capture, multimedia applications, teleconferencing, video editing, and human–computer interfaces. It can also be used in graduate courses on computer vision, image processing, real-time architecture, machine learning, or data mining.
Provides an introduction to recent techniques in multimedia semantic mining necessary to researchers new to the field.