Pattern Recognition and Computational Intelligence Techniques Using Matlab, E. S. Gopi
Автор: Concha Bielza, Pedro Larranaga Название: Data-Driven Computational Neuroscience: Machine Learning and Statistical Models ISBN: 110849370X ISBN-13(EAN): 9781108493703 Издательство: Cambridge Academ Рейтинг: Цена: 85530.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Data-driven computational neuroscience facilitates the transformation of data into insights into the structure and functions of the brain. This modern treatment of real world cases offers neuroscience researchers and graduate students a comprehensive, in-depth guide to statistical and machine learning methods.
Автор: Le Vuong Et Al Название: Face Processing And Applications To Distance Learning ISBN: 9814733024 ISBN-13(EAN): 9789814733021 Издательство: World Scientific Publishing Рейтинг: Цена: 68640.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание:
This special compendium provides a concise and unified vision of facial image processing. It addresses a collection of state-of-the-art techniques, covering the most important areas for facial biometrics and behavior analysis. These techniques also converge to serve an emerging practical application of interactive distance learning.
Readers will get a broad picture of the fundamental science of the field and technical details that make the research interesting. Moreover, the intellectual investigation motivated by the demand of real-life application will make this volume an inspiring read for current and prospective researchers and engineers in the fields of computer vision, machine learning and image processing.
Автор: Chen Chi Hau Название: Handbook Of Pattern Recognition And Computer Vision (5Th Edition) ISBN: 9814656526 ISBN-13(EAN): 9789814656528 Издательство: World Scientific Publishing Рейтинг: Цена: 195360.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Pattern recognition, image processing and computer vision are closely linked areas which have seen enormous progress in the last fifty years.
In the age of e-society, handwritten signature processing is an enabling technology in a multitude of fields in the "digital agenda" of many countries, ranging from e-health to e-commerce, from e-government to e-justice, from e-democracy to e-banking, and smart cities. Handwritten signatures are very complex signs; they are the result of an elaborate process that depends on the psychophysical state of the signer and the conditions under which the signature apposition process occurs. Notwithstanding, recent efforts from academies and industries now make possible the integration of signature-based technologies into other standard equipment to form complete solutions that are able to support the security requirements of today's society.
Advances in Digital Handwritten Signature Processing primarily provides an update on the most fascinating and valuable researches in the multifaceted field of handwritten signature analysis and processing. The chapters within also introduce and discuss critical aspects and precious opportunities related to the use of this technology, as well as highlight fundamental theoretical and applicative aspects of the field.
This book contains papers by well-recognized and active researchers and scientists, as well as by engineers and commercial managers working for large international companies in the field of signature-based systems for a wide range of applications and for the development of e-society.
This publication is devoted to both researchers and experts active in the field of biometrics and handwriting forensics, as well as professionals involved in the development of signature-based solutions for advanced applications in medicine, finance, commerce, banking, public and private administrations, etc. Handwritten Signature Processing may also be used as an advanced textbook by graduate students.
Автор: Pramod Kumar Pisharady; Prahlad Vadakkepat; Loh Ai Название: Computational Intelligence in Multi-Feature Visual Pattern Recognition ISBN: 9812870555 ISBN-13(EAN): 9789812870551 Издательство: Springer Рейтинг: Цена: 121890.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book presents a collection of computational intelligence algorithms that addresses issues in visual pattern recognition such as high computational complexity, abundance of pattern features, sensitivity to size and shape variations and poor performance against complex backgrounds.
Автор: Pramod Kumar Pisharady; Prahlad Vadakkepat; Loh Ai Название: Computational Intelligence in Multi-Feature Visual Pattern Recognition ISBN: 9811011710 ISBN-13(EAN): 9789811011719 Издательство: Springer Рейтинг: Цена: 104480.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book presents a collection of computational intelligence algorithms that addresses issues in visual pattern recognition such as high computational complexity, abundance of pattern features, sensitivity to size and shape variations and poor performance against complex backgrounds.
Автор: Pedrycz Название: Computational Intelligence for Pattern Recognition ISBN: 3319896288 ISBN-13(EAN): 9783319896281 Издательство: Springer Рейтинг: Цена: 139750.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: The book presents a comprehensive and up-to-date review of fuzzy pattern recognition. Since the inception of fuzzy sets, fuzzy pattern recognition with its methodology, algorithms, and applications, has offered new insights into the principles and practice of pattern classification.
Автор: Kristin J. Dana Название: Computational Texture and Patterns: From Textons to Deep Learning ISBN: 1681730111 ISBN-13(EAN): 9781681730110 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 51750.00 T Наличие на складе: Невозможна поставка. Описание: Visual pattern analysis is a fundamental tool in mining data for knowledge. Computational representations for patterns and texture allow us to summarize, store, compare, and label in order to learn about the physical world. Our ability to capture visual imagery with cameras and sensors has resulted in vast amounts of raw data, but using this information effectively in a task-specific manner requires sophisticated computational representations. We enumerate specific desirable traits for these representations: (1) intraclass invariance—to support recognition; (2) illumination and geometric invariance for robustness to imaging conditions; (3) support for prediction and synthesis to use the model to infer continuation of the pattern; (4) support for change detection to detect anomalies and perturbations; and (5) support for physics-based interpretation to infer system properties from appearance. In recent years, computer vision has undergone a metamorphosis with classic algorithms adapting to new trends in deep learning. This text provides a tour of algorithm evolution including pattern recognition, segmentation and synthesis. We consider the general relevance and prominence of visual pattern analysis and applications that rely on computational models.
Автор: Das Asit Kumar, Nayak Janmenjoy, Naik Bighnaraj Название: Computational Intelligence in Pattern Recognition: Proceedings of Cipr 2019 ISBN: 981139041X ISBN-13(EAN): 9789811390418 Издательство: Springer Рейтинг: Цена: 186330.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book presents practical development experiences in different areas of data analysis and pattern recognition, focusing on soft computing technologies, clustering and classification algorithms, rough set and fuzzy set theory, evolutionary computations, neural science and neural network systems, image processing, combinatorial pattern matching, social network analysis, audio and video data analysis, data mining in dynamic environments, bioinformatics, hybrid computing, big data analytics and deep learning. It also provides innovative solutions to the challenges in these areas and discusses recent developments.
Автор: Danel Jaso Название: Pattern Recognition Techniques, Technology & Applications ISBN: 1681174642 ISBN-13(EAN): 9781681174648 Издательство: Gazelle Book Services Рейтинг: Цена: 217350.00 T Наличие на складе: Невозможна поставка. Описание: This book highlights recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition. The book provides a comprehensive overview of the developments of techniques and approaches on pattern recognition. Pattern recognition is the science of making inferences from perceptual data, using tools from statistics, probability, computational geometry, machine learning, signal processing, and algorithm design. A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition task. Pattern recognition is the imposition of identity on input data, such as speech, images, or a stream of text, by the recognition and delineation of patterns it contains and their relationships. Stages in pattern recognition may involve measurement of the object to identify distinguishing attributes, extraction of features for the defining attributes, and comparison with known patterns to determine a match or mismatch. Pattern recognition has extensive application in astronomy, medicine, robotics, and remote sensing by satellites.
Автор: Sasikumar Gurumoorthy; Naresh Babu Muppalaneni; Xi Название: Computational Intelligence Techniques in Diagnosis of Brain Diseases ISBN: 9811065284 ISBN-13(EAN): 9789811065286 Издательство: Springer Рейтинг: Цена: 51230.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book highlights a new biomedical signal processing method of extracting a specific underlying signal from possibly noisy multi-channel recordings, and shows that the method is suitable for extracting independent components from the measured electroencephalogram (EEG) signal.
This updated compendium provides a methodical introduction with a coherent and unified repository of ensemble methods, theories, trends, challenges, and applications. More than a third of this edition comprised of new materials, highlighting descriptions of the classic methods, and extensions and novel approaches that have recently been introduced.
Along with algorithmic descriptions of each method, the settings in which each method is applicable and the consequences and tradeoffs incurred by using the method is succinctly featured. R code for implementation of the algorithm is also emphasized.
The unique volume provides researchers, students and practitioners in industry with a comprehensive, concise and convenient resource on ensemble learning methods.
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