Machine Learning for Audio, Image and Video Analysis, Camastra Francesco
Автор: Christopher M. Bishop Название: Pattern Recognition and Machine Learning ISBN: 0387310738 ISBN-13(EAN): 9780387310732 Издательство: Springer Рейтинг: Цена: 11878.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.
Автор: Miroslav Kubat Название: An Introduction to Machine Learning ISBN: 3319348868 ISBN-13(EAN): 9783319348865 Издательство: Springer Рейтинг: Цена: 6986.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book presents basic ideas of machine learning in a way that is easy to understand, by providing hands-on practical advice, using simple examples, and motivating students with discussions of interesting applications.
Описание: The book introduces novel Bayesian topic models for detection of events that are different from typical activities and a novel framework for change point detection for identifying sudden behavioural changes.Behaviour analysis and anomaly detection are key components of intelligent vision systems.
Описание: This textbook is an overview of theories, methodologies, and recent developments in the field, covering the theoretical foundation and providing a complete summary of the latest advances. It also presents key issues to be considered in making a real system.
Автор: Zhou, Kevin Название: Deep Learning for Medical Image Analysis ISBN: 0128104082 ISBN-13(EAN): 9780128104088 Издательство: Elsevier Science Рейтинг: Цена: 16505.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Deep learning is providing exciting solutions for medical image analysis problems and is seen as a key method for future applications. This book gives a clear understanding of the principles and methods of neural network and deep learning concepts, showing how the algorithms that integrate deep learning as a core component have been applied to medical image detection, segmentation and registration, and computer-aided analysis, using a wide variety of application areas.
Deep Learning for Medical Image Analysis is a great learning resource for academic and industry researchers in medical imaging analysis, and for graduate students taking courses on machine learning and deep learning for computer vision and medical image computing and analysis.
This book constitutes the refereed joint proceedings of the Third International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2017, and the 6th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2017, held in conjunction with the 20th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2017, in Quebec City, QC, Canada, in September 2017.
The 38 full papers presented at DLMIA 2017 and the 5 full papers presented at ML-CDS 2017 were carefully reviewed and selected. The DLMIA papers focus on the design and use of deep learning methods in medical imaging. The ML-CDS papers discuss new techniques of multimodal mining/retrieval and their use in clinical decision support.
Автор: Milan Sonka; Vaclav Hlavac; Roger Boyle Название: Image Processing, Analysis and Machine Vision ISBN: 0412455706 ISBN-13(EAN): 9780412455704 Издательство: Springer Рейтинг: Цена: 12157.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book reflects the authors` experience in teaching one and two semester undergraduate and graduate courses in Digital Image Processing, Digital Image Analysis, Machine Vision, Pattern Recognition and Intelligent Robotics at their respective institutions.
Автор: Guillermo Sapiro Название: Geometric Partial Differential Equations and Image Analysis ISBN: 0521685079 ISBN-13(EAN): 9780521685078 Издательство: Cambridge Academ Цена: 8078.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Researchers and practitioners will be able to achieve state-of-the-art practical results in a large number of real problems with the techniques described here. Applications covered include image segmentation, shape analysis, image enhancement, and tracking.
Автор: Kwa?nicka Название: Bridging the Semantic Gap in Image and Video Analysis ISBN: 3319738909 ISBN-13(EAN): 9783319738901 Издательство: Springer Рейтинг: Цена: 6986.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Semantic Gap in Image and Video Analysis: An Introduction.- Low-Level Feature Detectors and Descriptors for Smart Image and Video Analysis: A Comparative Study.- Scale-insensitive MSER Features: A Promising Tool for Meaningful Segmentation of Images.- Active Partitions in Localization of Semantically Important Image Structures.- Model-based 3D Object recognition in RGB-D Images.- Ontology-Based Structured Video Annotation for Content-Based Video Retrieval via Spatiotemporal Reasoning.- Deep Learning - a New Era in Bridging the Semantic Gap.
Автор: Zin Название: Big Data Analysis and Deep Learning Applications ISBN: 9811308683 ISBN-13(EAN): 9789811308680 Издательство: Springer Рейтинг: Цена: 25155.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Big data analysis.- Machine learning and applications.- Monitoring system by using image processing.- Conventional neural networks and its applications.- Information and communication.- Industrial information systems and applications.
Автор: Liang Wang; Guoying Zhao; Li Cheng; Matti Pietik?i Название: Machine Learning for Vision-Based Motion Analysis ISBN: 1447126076 ISBN-13(EAN): 9781447126072 Издательство: Springer Рейтинг: Цена: 23058.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Based on contributions to the International Workshop on Machine Learning for Vision-Based Motion Analysis, this volume highlights the latest algorithms and systems for robust and effective vision-based motion understanding.
Описание: 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.
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