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Author: David Tomas
Publisher: A&C Black
ISBN: 1441187650
Category : Philosophy
Languages : en
Pages : 241
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Book Description
Beyond the Image Machine is an eloquent and stimulating argument for an alternative history of scientific and technological imaging systems. Drawing on a range of hitherto and marginalised examples from the world of visual representation and the work of key theorists and thinkers, such as Latour, de Certeau, McLuhan and Barthes, David Tomas offers a disarticulated and deviant view of the relationship between archaic and new representations, imaging technologies and media induced experience. Rejecting the possibility of absolute forms of knowledge, Tomas shows how new media technologies have changed the nature of established disciplines. The book develops Tomas's own theory of transcultural space and makes several original contributions to current debates on the culture of advanced technology.
Author: David Tomas
Publisher: A&C Black
ISBN: 1441187650
Category : Philosophy
Languages : en
Pages : 241
Get Book Here
Book Description
Beyond the Image Machine is an eloquent and stimulating argument for an alternative history of scientific and technological imaging systems. Drawing on a range of hitherto and marginalised examples from the world of visual representation and the work of key theorists and thinkers, such as Latour, de Certeau, McLuhan and Barthes, David Tomas offers a disarticulated and deviant view of the relationship between archaic and new representations, imaging technologies and media induced experience. Rejecting the possibility of absolute forms of knowledge, Tomas shows how new media technologies have changed the nature of established disciplines. The book develops Tomas's own theory of transcultural space and makes several original contributions to current debates on the culture of advanced technology.
Author: David Tomas
Publisher: A&C Black
ISBN: 0826462723
Category : Philosophy
Languages : en
Pages : 240
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Book Description
Beyond the Image Machine: A History of Visual Technologies is an eloquent and stimulating argument for an alternative history of scientific and technological imaging systems. It explores the ways in which the technological medium through which a piece of visual art is rendered contributes significantly to the experience of the human looking at it. Through a series of studies of individual art works, David Tomas gives a fascinating and wholly original account of the relationship between visual technology and human sensory perception. Illustrated throughout, the book draws on a range of hitherto marginalised examples from the world of visual representation. In examining these art works and, it draws upon the work of such key theorists as Latour, de Certeau, Mc Luhan and Barthes. Beyond the Image Machine is an original and contribution to the study of visual culture and the technologies that mediate it. It is a book that changes the terms of the debate and redefines the discipline. Anyone studying, teaching or researching in this area will find it a rich source of ideas and inspiration.
Author: Jorge L.C. Sanz
Publisher: Springer Science & Business Media
ISBN: 3642582885
Category : Computers
Languages : en
Pages : 758
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Book Description
Image processing and machine vision are fields of renewed interest in the commercial market. People in industry, managers, and technical engineers are looking for new technologies to move into the market. Many of the most promising developments are taking place in the field of image processing and its applications. The book offers a broad coverage of advances in a range of topics in image processing and machine vision.
Author: Jan Romportl
Publisher: Springer
ISBN: 3319096680
Category : Technology & Engineering
Languages : en
Pages : 218
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Book Description
This book is an edited collection of chapters based on the papers presented at the conference “Beyond AI: Artificial Dreams” held in Pilsen in November 2012. The aim of the conference was to question deep-rooted ideas of artificial intelligence and cast critical reflection on methods standing at its foundations. Artificial Dreams epitomize our controversial quest for non-biological intelligence and therefore the contributors of this book tried to fully exploit such a controversy in their respective chapters, which resulted in an interdisciplinary dialogue between experts from engineering, natural sciences and humanities. While pursuing the Artificial Dreams, it has become clear that it is still more and more difficult to draw a clear divide between human and machine. And therefore this book tries to portrait such an image of what lies beyond artificial intelligence: we can see the disappearing human-machine divide, a very important phenomenon of nowadays technological society, the phenomenon which is often uncritically praised, or hypocritically condemned. And so this phenomenon found its place in the subtitle of the whole volume as well as in the title of the chapter of Kevin Warwick, one of the keynote speakers at “Beyond AI: Artificial Dreams”.
Author: Yixin Chen
Publisher: Springer Science & Business Media
ISBN: 1402080344
Category : Technology & Engineering
Languages : en
Pages : 194
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Book Description
In the early 1990s, the establishment of the Internet brought forth a revolutionary viewpoint of information storage, distribution, and processing: the World Wide Web is becoming an enormous and expanding distributed digital library. Along with the development of the Web, image indexing and retrieval have grown into research areas sharing a vision of intelligent agents. Far beyond Web searching, image indexing and retrieval can potentially be applied to many other areas, including biomedicine, space science, biometric identification, digital libraries, the military, education, commerce, culture and entertainment. Machine Learning and Statistical Modeling Approaches to Image Retrieval describes several approaches of integrating machine learning and statistical modeling into an image retrieval and indexing system that demonstrates promising results. The topics of this book reflect authors' experiences of machine learning and statistical modeling based image indexing and retrieval. This book contains detailed references for further reading and research in this field as well.
Author: Sambhunath Biswas
Publisher: Springer Science & Business Media
ISBN: 1846289572
Category : Computers
Languages : en
Pages : 250
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Book Description
This book deals with various image processing and machine vision problems efficiently with splines and includes: the significance of Bernstein Polynomial in splines, detailed coverage of Beta-splines applications which are relatively new, Splines in motion tracking, various deformative models and their uses. Finally the book covers wavelet splines which are efficient and effective in different image applications.
Author: Terry Caelli
Publisher: Springer Science & Business Media
ISBN: 1489918167
Category : Technology & Engineering
Languages : en
Pages : 441
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Book Description
In this groundbreaking new volume, computer researchers discuss the development of technologies and specific systems that can interpret data with respect to domain knowledge. Although the chapters each illuminate different aspects of image interpretation, all utilize a common approach - one that asserts such interpretation must involve perceptual learning in terms of automated knowledge acquisition and application, as well as feedback and consistency checks between encoding, feature extraction, and the known knowledge structures in a given application domain. The text is profusely illustrated with numerous figures and tables to reinforce the concepts discussed.
Author: Michael Beyeler
Publisher: Packt Publishing Ltd
ISBN: 178398029X
Category : Computers
Languages : en
Pages : 368
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Book Description
Expand your OpenCV knowledge and master key concepts of machine learning using this practical, hands-on guide. About This Book Load, store, edit, and visualize data using OpenCV and Python Grasp the fundamental concepts of classification, regression, and clustering Understand, perform, and experiment with machine learning techniques using this easy-to-follow guide Evaluate, compare, and choose the right algorithm for any task Who This Book Is For This book targets Python programmers who are already familiar with OpenCV; this book will give you the tools and understanding required to build your own machine learning systems, tailored to practical real-world tasks. What You Will Learn Explore and make effective use of OpenCV's machine learning module Learn deep learning for computer vision with Python Master linear regression and regularization techniques Classify objects such as flower species, handwritten digits, and pedestrians Explore the effective use of support vector machines, boosted decision trees, and random forests Get acquainted with neural networks and Deep Learning to address real-world problems Discover hidden structures in your data using k-means clustering Get to grips with data pre-processing and feature engineering In Detail Machine learning is no longer just a buzzword, it is all around us: from protecting your email, to automatically tagging friends in pictures, to predicting what movies you like. Computer vision is one of today's most exciting application fields of machine learning, with Deep Learning driving innovative systems such as self-driving cars and Google's DeepMind. OpenCV lies at the intersection of these topics, providing a comprehensive open-source library for classic as well as state-of-the-art computer vision and machine learning algorithms. In combination with Python Anaconda, you will have access to all the open-source computing libraries you could possibly ask for. Machine learning for OpenCV begins by introducing you to the essential concepts of statistical learning, such as classification and regression. Once all the basics are covered, you will start exploring various algorithms such as decision trees, support vector machines, and Bayesian networks, and learn how to combine them with other OpenCV functionality. As the book progresses, so will your machine learning skills, until you are ready to take on today's hottest topic in the field: Deep Learning. By the end of this book, you will be ready to take on your own machine learning problems, either by building on the existing source code or developing your own algorithm from scratch! Style and approach OpenCV machine learning connects the fundamental theoretical principles behind machine learning to their practical applications in a way that focuses on asking and answering the right questions. This book walks you through the key elements of OpenCV and its powerful machine learning classes, while demonstrating how to get to grips with a range of models.
Author: Paul Virilio
Publisher:
ISBN: 9780851704456
Category : Art
Languages : en
Pages : 100
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Book Description
No Marketing Blurb
Author: Guorong Wu
Publisher: Academic Press
ISBN: 0128041145
Category : Computers
Languages : en
Pages : 514
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Book Description
Machine Learning and Medical Imaging presents state-of- the-art machine learning methods in medical image analysis. It first summarizes cutting-edge machine learning algorithms in medical imaging, including not only classical probabilistic modeling and learning methods, but also recent breakthroughs in deep learning, sparse representation/coding, and big data hashing. In the second part leading research groups around the world present a wide spectrum of machine learning methods with application to different medical imaging modalities, clinical domains, and organs. The biomedical imaging modalities include ultrasound, magnetic resonance imaging (MRI), computed tomography (CT), histology, and microscopy images. The targeted organs span the lung, liver, brain, and prostate, while there is also a treatment of examining genetic associations. Machine Learning and Medical Imaging is an ideal reference for medical imaging researchers, industry scientists and engineers, advanced undergraduate and graduate students, and clinicians. - Demonstrates the application of cutting-edge machine learning techniques to medical imaging problems - Covers an array of medical imaging applications including computer assisted diagnosis, image guided radiation therapy, landmark detection, imaging genomics, and brain connectomics - Features self-contained chapters with a thorough literature review - Assesses the development of future machine learning techniques and the further application of existing techniques
