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Graphics of large datasets : visualizing a million / par Unwin, Antony. Publication : [S.l.] : Springer, 2006 . 271 p. ; , Graphics are great for exploring data, but how can they be used for looking at the large datasets that are commonplace to-day? This book shows how to look at ways of visualizing large datasets, whether large in numbers of cases or large in numbers of variables or large in both. Data visualization is useful for data cleaning, exploring data, identifying trends and clusters, spotting local patterns, evaluating modeling output, and presenting results. It is essential for exploratory data analysis and data mining. Data analysts, statisticians, computer scientists-indeed anyone who has to explore a large dataset of their own-should benefit from reading this book. New approaches to graphics are needed to visualize the information in large datasets and most of the innovations described in this book are developments of standard graphics. There are considerable advantages in extending displays which are well-known and well-tried, both in understanding how best to make use of them in your work and in presenting results to others. It should also make the book readily accessible for readers who already have a little experience of drawing statistical graphics. All ideas are illustrated with displays from analyses of real datasets and the authors emphasize the importance of interpreting displays effectively. Graphics should be drawn to convey information and the book includes many insightful examples. From the reviews: "Anyone interested in modern techniques for visualizing data will be well rewarded by reading this book. There is a wealth of important plotting types and techniques." Paul Murrell for the Journal of Statistical Software, December 2006 "This fascinating book looks at the question of visualizing large datasets from many different perspectives. Different authors are responsible for different chapters and this approach works well in giving the reader alternative viewpoints of the same problem. Interestingly the authors have cleverly chosen a definition of 'large dataset'. Essentially they focus on datasets with the order of a million cases. As the authors point out there are now many examples of much larger datasets but by limiting to ones that can be loaded in their entirety in standard statistical software they end up with a book that has great utility to the practitioner rather than just the theorist. Another very attractive feature of the book is the many colour plates, showing clearly what can now routinely be seen on the computer screen. The interactive nature of data analysis with large datasets is hard to reproduce in a book but the authors make an excellent attempt to do just this." P. Marriott for the Short Book Reviews of the ISI. 24 cm. Date : 2006 Disponibilité : Exemplaires disponibles: La bibliothèque des sciences de l'ingénieur (1),

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Handbook of computer vision algorithms in image algebra par Ritter, G. X. Publication : Boca Raton CRC Press 2001 . 417 pages 26 cm. Date : 2001 Disponibilité : Exemplaires disponibles: La bibliothèque des sciences de l'ingénieur (1),

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Image correlation for shape, motion and deformation measurements basic concepts, theory and applications par Sutton, Michael Albert Publication : New York, N.Y. Springer 2009 . xx, 321 pages , "... a comprehensive overview of data extraction through image analysis ... with equal treatment of computer vision fundamentals and techniques for practical applications"--P. [4] of cover. 25 cm. Date : 2009 Disponibilité : Exemplaires disponibles: La bibliothèque des sciences de l'ingénieur (1),

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Image processing and mathematical morphology fundamentals and applications par Shih, Frank Y. Publication : Boca Raton CRC Press 2009 . xxiii, 415 pages 25 cm. Date : 2009 Disponibilité : Exemplaires disponibles: La bibliothèque des sciences de l'ingénieur (1),

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Image processing and pattern recognition : fundamentals and techniques par Shih, Frank Y. Publication : [S.l.] Wiley-IEEE Press 2010 . 552 p. , A comprehensive guide to the essential principles of image processing and pattern recognition Techniques and applications in the areas of image processing and pattern recognition are growing at an unprecedented rate. Containing the latest state-of-the-art developments in the field, Image Processing and Pattern Recognition presents clear explanations of the fundamentals as well as the most recent applications. It explains the essential principles so readers will not only be able to easily implement the algorithms and techniques, but also lead themselves to discover new problems and applications. Unlike other books on the subject, this volume presents numerous fundamental and advanced image processing algorithms and pattern recognition techniques to illustrate the framework. Scores of graphs and examples, technical assistance, and practical tools illustrate the basic principles and help simplify the problems, allowing students as well as professionals to easily grasp even complicated theories. It also features unique coverage of the most interesting developments and updated techniques, such as image watermarking, digital steganography, document processing and classification, solar image processing and event classification, 3-D Euclidean distance transformation, shortest path planning, soft morphology, recursive morphology, regulated morphology, and sweep morphology. Additional topics include enhancement and segmentation techniques, active learning, feature extraction, neural networks, and fuzzy logic. Featuring supplemental materials for instructors and students, Image Processing and Pattern Recognition is designed for undergraduate seniors and graduate students, engineering and scientific researchers, and professionals who work in signal processing, image processing, pattern recognition, information security, document processing, multimedia systems, and solar physics. 24 cm. Date : 2010 Disponibilité : Exemplaires disponibles: La bibliothèque des sciences de l'ingénieur (1),

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Image processing for computer graphics and vision / par Velho, Luiz. Publication : London : Springer, 2009 . xv, 463 pages : , Revised edition of: Image processing for computer graphics / Jonas Gomes, Luiz Velho. c1997. 24 cm. Date : 2009 Disponibilité : Exemplaires disponibles: La bibliothèque des sciences de l'ingénieur (1),

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Image Recognition and Classification: Algorithms, Systems, and Applications. Vol. 78 par Javidi, Bahram, Publication : Boca Raton : | Florence : C R C Press LLC | Taylor & Francis Group [distributor] 2002 . 1 online resource (520 pages) Date : 2002 Disponibilité : Exemplaires disponibles: La bibliothèque des sciences de l'ingénieur (1),

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Image segmentation and compression using hidden Markov models / par Li,, Jia, Publication : Boston, MA : Kluwer Academic, 2000 . xii, 141 p. : 25 cm. Date : 2000 Disponibilité : Exemplaires disponibles: La bibliothèque des sciences de l'ingénieur (1),

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Image structure par Florack,, Luc. Publication : [S.l.] Springer 2009 . 271 p. , Despite the fact that images constitute the main objects in computer vision and image analysis, there is remarkably little concern about their actual definition. In this book a complete account of image structure is proposed in terms of rigorously defined machine concepts, using basic tools from algebra, analysis, and differential geometry. Machine technicalities such as discretisation and quantisation details are de-emphasised, and robustness with respect to noise is manifest. From the foreword by Jan Koenderink: ` It is my hope that the book will find a wide audience, including physicists - who still are largely unaware of the general importance and power of scale space theory, mathematicians - who will find in it a principled and formally tight exposition of a topic awaiting further development, and computer scientists - who will find here a unified and conceptually well founded framework for many apparently unrelated and largely historically motivated methods they already know and love. The book is suited for self-study and graduate courses, the carefully formulated exercises are designed to get to grips with the subject matter and prepare the reader for original research. ' 23 cm. Date : 2009 Disponibilité : Exemplaires disponibles: La bibliothèque des sciences de l'ingénieur (1),

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Image-based modeling par Quan, Long Publication : New York Springer. 2010 . xviii, 251 pages 25 cm. Date : 2010 Disponibilité : Exemplaires disponibles: La bibliothèque des sciences de l'ingénieur (2),

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Machine learning for multimedia content analysis par Gong,, Yihong. Publication : [S.l.] Springer 2010 . 277 p. , This volume introduces machine learning techniques that are particularly powerful and effective for modeling multimedia data and common tasks of multimedia content analysis. It systematically covers key machine learning techniques in an intuitive fashion and demonstrates their applications through case studies. Coverage includes examples of unsupervised learning, generative models and discriminative models. In addition, the book examines Maximum Margin Markov (M3) networks, which strive to combine the advantages of both the graphical models and Support Vector Machines (SVM). 24 cm. Date : 2010 Disponibilité : Exemplaires disponibles: La bibliothèque des sciences de l'ingénieur (1),

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Machine learning for vision-based motion analysis : theory and techniques   Publication : [S.l.] Springer 2010 . 384 p. , Techniques of vision-based motion analysis aim to detect, track, identify, and generally understand the behavior of objects in image sequences. With the growth of video data in a wide range of applications from visual surveillance to human-machine interfaces, the ability to automatically analyze and understand object motions from video footage is of increasing importance. Among the latest developments in this field is the application of statistical machine learning algorithms for object tracking, activity modeling, and recognition. Developed from expert contributions to the first and second International Workshop on Machine Learning for Vision-Based Motion Analysis, this important text/reference highlights the latest algorithms and systems for robust and effective vision-based motion understanding from a machine learning perspective. Highlighting the benefits of collaboration between the communities of object motion understanding and machine learning, the book discusses the most active forefronts of research, including current challenges and potential future directions. Topics and features: provides a comprehensive review of the latest developments in vision-based motion analysis, presenting numerous case studies on state-of-the-art learning algorithms; examines algorithms for clustering and segmentation, and manifold learning for dynamical models; describes the theory behind mixed-state statistical models, with a focus on mixed-state Markov models that take into account spatial and temporal interaction; discusses object tracking in surveillance image streams, discriminative multiple target tracking, and guidewire tracking in fluoroscopy; explores issues of modeling for saliency detection, human gait modeling, modeling of extremely crowded scenes, and behavior modeling from video surveillance data; investigates methods for automatic recognition of gestures in Sign Language, and human action recognition from small training sets. Researchers, professional engineers, and graduate students in computer vision, pattern recognition and machine learning, will all find this text an accessible survey of machine learning techniques for vision-based motion analysis. The book will also be of interest to all who work with specific vision applications, such as surveillance, sport event analysis, healthcare, video conferencing, and motion video indexing and retrieval. 24 cm. Date : 2010 Disponibilité : Exemplaires disponibles: La bibliothèque des sciences de l'ingénieur (1),

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Modeling and inverse problems in image analysis. par Chalmond, Bernard. Publication : New york Springer 2003 . 309 p. , More mathematicians have been taking part in the development of digital image processing as a science and the contributions are reflected in the increasingly important role modeling has played solving complex problems. This book is mostly concerned with energy-based models. Most of these models come from industrial projects in which the author was involved in robot vision and radiography: tracking 3D lines, radiographic image processing, 3D reconstruction and tomography, matching, deformation learning. Numerous graphical illustrations accompany the text. 24 cm. Date : 2003 Disponibilité : Exemplaires disponibles: La bibliothèque des sciences de l'ingénieur (1),

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