Catalogue Bibliothèque de L'IMIST/CNRST › Résultats de la recherche pour 'ccl=su:Image processing'

Votre recherche a retourné 230 résultats.

		Advances in Digital Image Processing and Information Technology First International Conference on Digital Image Processing and Pattern Recognition, DPPR 2011, Tirunelveli, Tamil Nadu, India, September 23-25, 2011. Proceedings / par Nagamalai, Dhinaharan. Publication : Berlin, Heidelberg : Springer Berlin Heidelberg : \| Springer e-books : \| Imprint: Springer : \| Springer e-books, 2011 . 1 online resource. Date : 2011 Disponibilité : Exemplaires disponibles: La bibliothèque des sciences de l'ingénieur (1), Réserver Ajouter au panier (Supprimer)
		Applications of pulse-coupled neural networks par Ma, Yide. Publication : [S.l.] Springer 2010 . 199 p. , "Applications of Pulse-Coupled Neural Networks" explores the fields of image processing, including image filtering, image segmentation, image fusion, image coding, image retrieval, and biometric recognition, and the role of pulse-coupled neural networks in these fields. This book is intended for researchers and graduate students in artificial intelligence, pattern recognition, electronic engineering, and computer science. Prof. Yide Ma conducts research on intelligent information processing, biomedical image processing, and embedded system development at the School of Information Science and Engineering, Lanzhou University, China. 24 cm. Date : 2010 Disponibilité : Exemplaires disponibles: La bibliothèque des sciences de l'ingénieur (1), Réserver Ajouter au panier (Supprimer)
		Boosting-based face detection and adaptation par Zhang,, Cha. Publication : [S.l.] Morgan and Claypool Publishers 2010 . 140 p. , Face detection, because of its vast array of applications, is one of the most active research areas in computer vision. In this book, we review various approaches to face detection developed in the past decade, with more emphasis on boosting-based learning algorithms. We then present a series of algorithms that are empowered by the statistical view of boosting and the concept of multiple instance learning. We start by describing a boosting learning framework that is capable to handle billions of training examples. It differs from traditional bootstrapping schemes in that no intermediate thresholds need to be set during training, yet the total number of negative examples used for feature selection remains constant and focused (on the poor performing ones). A multiple instance pruning scheme is then adopted to set the intermediate thresholds after boosting learning. This algorithm generates detectors that are both fast and accurate. Table of Contents: A Brief Survey of the Face Detection Literature / Cascade-based Real-Time Face Detection / Multiple Instance Learning for Face Detection / Detector Adaptation / Other Applications / Conclusions and Future Work. 24 cm. Date : 2010 Disponibilité : Exemplaires disponibles: La bibliothèque des sciences de l'ingénieur (1), Réserver Ajouter au panier (Supprimer)
		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), Réserver Ajouter au panier (Supprimer)
		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), Réserver Ajouter au panier (Supprimer)
		Tensors in image processing and computer vision Publication : [S.l.] Springer 2009 . 480 p. , Tensor signal processing is an emerging field with important applications to computer vision and image processing. This book presents the state of the art in this new branch of signal processing, offering a great deal of research and discussions by leading experts in the area. The wide-ranging volume offers an overview into cutting-edge research into the newest tensor processing techniques and their application to different domains related to computer vision and image processing. This comprehensive text will prove to be an invaluable reference and resource for researchers, practitioners and advanced students working in the area of computer vision and image processing. 24 cm. Date : 2009 Disponibilité : Exemplaires disponibles: La bibliothèque des sciences de l'ingénieur (1), Réserver Ajouter au panier (Supprimer)
		Vision with direction : a systematic introduction to image processing and computer vision par Bigun, Josef. Publication : [S.l.] Springer 2010 . 396 p. , This introductory textbook presents the modern signal processing concepts used in computer vision and image analysis in a systematic and mathematically coherent way. For the first time in a textbook on image processing, single direction, group direction, corners and edges, Hough transform, and motion estimation are developed in a principled way using direction tensors as the unifying concept. The topics presented include Hilbert spaces, the Fourier transform, scale analysis, direction fields, structure tensors, motion tensors, the Hough transform, grouping, and segmentation. Directional signal processing, an increasingly crucial element of computer vision for which neural circuits exist in human vision, is dealt with in depth by use of tensors. All chapters are richly illustrated, with color graphics from cover to cover; applications are studied in various fields, including biometric person authentication, texture analysis, optical character recognition, and motion estimation and tracking; and exercises help the sudent verify progress. Developed out of courses given by the author, this introductory textbook addresses advanced undergarduates as well as master and PhD students in computer science, engineering, mathematics, and in other disciplines where techniques from computer vision, image processing, visual computation and signal analysis are applied. 24 cm. Date : 2010 Disponibilité : Exemplaires disponibles: La bibliothèque des sciences de l'ingénieur (1), Réserver Ajouter au panier (Supprimer)
		Image and signal processing : 5th International Conference, ICISP 2012, Agadir, Morocco, June 28-30, 2012. Proceedings / Publication : Berlin ; \| New York : Springer, 2012 . 1 online resource (xix, 599 pages) : , International conference proceedings. Date : 2012 Disponibilité : Exemplaires disponibles: La bibliothèque des sciences de l'ingénieur (1), Réserver Ajouter au panier (Supprimer)
		Handbook of Document Image Processing and Recognition Publication : . 339 illus., 159 illus. in color. eReference. Disponibilité : http://dx.doi.org/10.1007/978-0-85729-859-1, Ajouter au panier (Supprimer)
		Computer Vision Metrics Survey, Taxonomy, and Analysis / par Krig, Scott. Publication : . XXXI, 508 p. 216 illus. Disponibilité : http://dx.doi.org/10.1007/978-1-4302-5930-5, Ajouter au panier (Supprimer)
		3D Computer Vision Efficient Methods and Applications / par Wöhler, Christian. Publication : . XVIII, 382 p. Disponibilité : http://dx.doi.org/10.1007/978-1-4471-4150-1, Ajouter au panier (Supprimer)
		Crowd Simulation par Thalmann, Daniel. Publication : . XV, 296 p. 175 illus., 147 illus. in color. Disponibilité : http://dx.doi.org/10.1007/978-1-4471-4450-2, Ajouter au panier (Supprimer)
		Embedded Multimedia Security Systems Algorithms and Architectures / par Pande, Amit. Publication : . XVIII, 146 p. Disponibilité : http://dx.doi.org/10.1007/978-1-4471-4459-5, Ajouter au panier (Supprimer)
		Image and Video-Based Artistic Stylisation Publication : . XII, 399 p. 217 illus., 158 illus. in color. Disponibilité : http://dx.doi.org/10.1007/978-1-4471-4519-6, Ajouter au panier (Supprimer)
		Consumer Depth Cameras for Computer Vision Research Topics and Applications / Publication : . XVI, 210 p. 109 illus., 106 illus. in color. Disponibilité : http://dx.doi.org/10.1007/978-1-4471-4640-7, Ajouter au panier (Supprimer)
		Imaging Spectroscopy for Scene Analysis par Robles-Kelly, Antonio. Publication : . XVIII, 270 p. Disponibilité : http://dx.doi.org/10.1007/978-1-4471-4652-0, Ajouter au panier (Supprimer)
		Time-of-Flight Cameras Principles, Methods and Applications / par Hansard, Miles. Publication : . X, 96 p. 55 illus., 19 illus. in color. Disponibilité : http://dx.doi.org/10.1007/978-1-4471-4658-2, Ajouter au panier (Supprimer)
		Visual Texture Accurate Material Appearance Measurement, Representation and Modeling / par Haindl, Michal. Publication : . XXXI, 284 p. 158 illus., 148 illus. in color. Disponibilité : http://dx.doi.org/10.1007/978-1-4471-4902-6, Ajouter au panier (Supprimer)
		Efficient Algorithms for Discrete Wavelet Transform With Applications to Denoising and Fuzzy Inference Systems / par Shukla, K. K. Publication : . IX, 91 p. 46 illus., 31 illus. in color. Disponibilité : http://dx.doi.org/10.1007/978-1-4471-4941-5, Ajouter au panier (Supprimer)
		Omnidirectional Vision Systems Calibration, Feature Extraction and 3D Information / par Puig, Luis. Publication : . XI, 122 p. 68 illus., 35 illus. in color. Disponibilité : http://dx.doi.org/10.1007/978-1-4471-4947-7, Ajouter au panier (Supprimer)