Автор: Margarita N. Favorskaya; Lakhmi C. Jain Название: Computer Vision in Advanced Control Systems-5 ISBN: 3030337944 ISBN-13(EAN): 9783030337940 Издательство: Springer Рейтинг: Цена: 13974.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book applies novel theories to improve algorithms in complex data analysis in various fields, including object detection, remote sensing, data transmission, data fusion, gesture recognition, and medical image processing and analysis. It is intended for Ph.D.
Автор: Lukac R. Название: Computational Photography ISBN: 1439817499 ISBN-13(EAN): 9781439817490 Издательство: Taylor&Francis Рейтинг: Цена: 27562.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Details advances in computational photography, their applications, and specific research challenges. This text begins with computational photography fundamentals and computational cameras, followed by a discussion of computational photography approaches across a broad spectrum of imaging applications.
Автор: Amir R. Zamir et al Название: Large-Scale Visual Geo-Localization ISBN: 331925779X ISBN-13(EAN): 9783319257792 Издательство: Springer Рейтинг: Цена: 16769.00 р. Наличие на складе: Поставка под заказ.
Описание: Thistimely and authoritative volume explores the bidirectional relationship betweenimages and locations. The text presents a comprehensive review of the state ofthe art in large-scale visual geo-localization, and discusses the emergingtrends in this area. Valuable insights are supplied by a pre-eminent selectionof experts in the field, into a varied range of real-world applications ofgeo-localization. Topics and features: discusses the latest methods to exploitinternet-scale image databases for devising geographically rich features andgeo-localizing query images at different scales; investigates geo-localizationtechniques that are built upon high-level and semantic cues; describes methodsthat perform precise localization by geometrically aligning the query imageagainst a 3D model; reviews techniques that accomplish image understandingassisted by the geo-location, as well as several approaches for geo-localizationunder practical, real-world settings.
Автор: Radu Tudor Ionescu; Marius Popescu Название: Knowledge Transfer between Computer Vision and Text Mining ISBN: 3319303651 ISBN-13(EAN): 9783319303659 Издательство: Springer Рейтинг: Цена: 16769.00 р. Наличие на складе: Поставка под заказ.
Описание: This ground-breaking text/reference divergesfrom the traditional view that computer vision (for image analysis) and stringprocessing (for text mining) are separate and unrelated fields of study,propounding that images and text can be treated in a similar manner for thepurposes of information retrieval, extraction and classification.
Автор: Marco, Leo Название: Computer Vision for Assistive Healthcare ISBN: 0128134453 ISBN-13(EAN): 9780128134450 Издательство: Elsevier Science Рейтинг: Цена: 14317.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Computer Vision for Assistive Healthcare describes how advanced computer vision techniques provide tools to support common human needs such as mental functioning, personal mobility, sensory functions and daily living activities. It describes how computer vision techniques - such as image processing, pattern recognition, machine learning and Language Processing and Computer Graphics- can cooperate with Robotics to provide such tools. The range of application areas covered are:
Mental functioning
Personal mobility
Sensory functions
Daily living activities
The reader will learn:
What are the emerging computer vision techniques for supporting mental functioning and to develop a Socially Assistive Robot (SAR)
The algorithms for analysing human behaviour beginning with visual data derived from environmental and wearable sensors ('first person vision')
How smart interfaces and virtual reality tools lead to the development of advanced rehabilitation systems able to perform human action and activity recognition using vision based techniques allowing a natural interaction experience
How Robotics Agent Coachers that can assist people with movement disorders during the execution of motor exercises
The technology behind intelligent wheelchairs
How computer vision technologies have the potential to assist blind people to independently access, understand, and explore the environments (both indoor and outdoor)
Computer vision-based solutions recently employed for safety and health monitoring: Fall detection, life log, and vital parameter monitoring
The first book to give the state-of-the-art computer vision techniques and tools for assistive healthcare
Broad range of topic areas ranging from Image processing, pattern recognition, machine learning to robotics, natural language processing and computer graphics
Wide range of application areas ranging from mobility, sensory substitution, safety and security, to mental and physical rehabilitation and training
Written by leading researchers in this growing field of research
Contains pieces of code
Describes the outstanding research challenges still to be tackled, giving researchers good indicators of research opportunities
Автор: Wang, Zhangyang Название: Deep Learning Through Sparse and Low-Rank Modeling ISBN: 0128136596 ISBN-13(EAN): 9780128136591 Издательство: Elsevier Science Рейтинг: Цена: 13304.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Deep Learning through Sparse Representation and Low-Rank Modeling bridges classical sparse and low rank models--those that emphasize problem-specific Interpretability--with recent deep network models that have enabled a larger learning capacity and better utilization of Big Data. It shows how the toolkit of deep learning is closely tied with the sparse/low rank methods and algorithms, providing a rich variety of theoretical and analytic tools to guide the design and interpretation of deep learning models. The development of the theory and models is supported by a wide variety of applications in computer vision, machine learning, signal processing, and data mining.
This book will be highly useful for researchers, graduate students and practitioners working in the fields of computer vision, machine learning, signal processing, optimization and statistics.
Combines classical sparse and low-rank models and algorithms with the latest advances in deep learning networks
Shows how the structure and algorithms of sparse and low-rank methods improves the performance and interpretability of Deep Learning models
Provides tactics on how to build and apply customized deep learning models for various applications
Автор: Chen, Mei Название: Computer Vision for Microscopy Image Analysis ISBN: 0128149728 ISBN-13(EAN): 9780128149720 Издательство: Elsevier Science Рейтинг: Цена: 17854.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
High-throughput microscopy enables researchers to acquire thousands of images automatically over a short time, making it possible to conduct large-scale, image-based experiments for biological or biomedical discovery. However, visual analysis of large-scale image data is a daunting task. The post-acquisition component of high-throughput microscopy experiments calls for effective and efficient computer vision techniques.
Computer Vision for Microscopy Image Analysis provides a comprehensive and in-depth introduction to state-of-the-art computer vision techniques for microscopy image analysis, demonstrating how they can be effectively applied to biological and medical data.
The reader of the book will learn:
How computer vision analysis can automate and enhance human assessment of microscopy images for discovery
The important steps in microscopy image analysis
State-of-the-art methods for microscopy image analysis including machine learning and deep neural network approaches
This reference on the state-of-the-art computer vision methods in microscopy image analysis is suitable for researchers and graduate students interested in analyzing microscopy images or for developing toolsets for general biomedical image analysis applications.
Each topic contains a comprehensive overview of the field, followed by in-depth presentation of a state-of-the-art approach
Perspectives and content contributed by both technologists and biologists
Tackles specific problems of detection, segmentation, classification, tracking, cellular event detection
Contains the fundamentals of object measurement in microscopy images
Contains open source data and toolsets for microscopy image analysis on an accompanying website
Автор: Rastislav Lukac Название: Perceptual Digital Imaging ISBN: 1138077402 ISBN-13(EAN): 9781138077409 Издательство: Taylor&Francis Рейтинг: Цена: 12554.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Visual perception is a complex process requiring interaction between the receptors in the eye that sense the stimulus and the neural system and the brain that are responsible for communicating and interpreting the sensed visual information. This process involves several physical, neural, and cognitive phenomena whose understanding is essential to design effective and computationally efficient imaging solutions. Building on advances in computer vision, image and video processing, neuroscience, and information engineering, perceptual digital imaging greatly enhances the capabilities of traditional imaging methods.
Filling a gap in the literature, Perceptual Digital Imaging: Methods and Applications comprehensively covers the system design, implementation, and application aspects of this emerging specialized area. It gives readers a strong, fundamental understanding of theory and methods, providing a foundation on which solutions for many of the most interesting and challenging imaging problems can be built.
The book features contributions by renowned experts who present the state of the art and recent trends in image acquisition, processing, storage, display, and visual quality evaluation. They detail advances in the field and explore human visual system-driven approaches across a broad spectrum of applications, including:
Image quality and aesthetics assessment
Digital camera imaging
White balancing and color enhancement
Thumbnail generation
Image restoration
Super-resolution imaging
Digital halftoning and dithering
Color feature extraction
Semantic multimedia analysis and processing
Video shot characterization
Image and video encryption
Display quality enhancement
This is a valuable resource for readers who want to design and implement more effective solutions for cutting-edge digital imaging, computer vision, and multimedia applications. Suitable as a graduate-level textbook or stand-alone reference for researchers and practitioners, it provides a unique overview of an important and rapidly developing research field.
Автор: Bertalmio, Marcelo (associate Professor, Informati Название: Vision models for high dynamic range and wide colour gamut imaging ISBN: 0128138947 ISBN-13(EAN): 9780128138946 Издательство: Elsevier Science Рейтинг: Цена: 19370.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
To enhance the overall viewing experience (for cinema, TV, games, AR/VR) the media industry is continuously striving to improve image quality. Currently the emphasis is on High Dynamic Range (HDR) and Wide Colour Gamut (WCG) technologies, which yield images with greater contrast and more vivid colours. The uptake of these technologies, however, has been hampered by the significant challenge of understanding the science behind visual perception. Vision Models for High Dynamic Range and Wide Colour Gamut Imaging provides university researchers and graduate students in computer science, computer engineering, vision science, as well as industry R&D engineers, an insight into the science and methods for HDR and WCG. It presents the underlying principles and latest practical methods in a detailed and accessible way, highlighting how the use of vision models is a key element of all state-of-the-art methods for these emerging technologies.
Presents the underlying vision science principles and models that are essential to the emerging technologies of HDR and WCG.
Explores state-of-the-art techniques for tone and gamut mapping.
Discusses open challenges and future directions of HDR and WCG research.
Описание: This book reviews the state of the art in deep learning approaches to high-performance robust disease detection, robust and accurate organ segmentation in medical image computing (radiological and pathological imaging modalities), and the construction and mining of large-scale radiology databases.
This book provides the mathematical fundamentals of linear algebra to practicers in computer vision, machine learning, robotics, applied mathematics, and electrical engineering. By only assuming a knowledge of calculus, the authors develop, in a rigorous yet down to earth manner, the mathematical theory behind concepts such as: vectors spaces, bases, linear maps, duality, Hermitian spaces, the spectral theorems, SVD, and the primary decomposition theorem. At all times, pertinent real-world applications are provided. This book includes the mathematical explanations for the tools used which we believe that is adequate for computer scientists, engineers and mathematicians who really want to do serious research and make significant contributions in their respective fields.
The book presents selected methods for accelerating image retrieval and classification in large collections of images using what are referred to as ‘hand-crafted features.’ It introduces readers to novel rapid image description methods based on local and global features, as well as several techniques for comparing images.
Developing content-based image comparison, retrieval and classification methods that simulate human visual perception is an arduous and complex process. The book’s main focus is on the application of these methods in a relational database context. The methods presented are suitable for both general-type and medical images. Offering a valuable textbook for upper-level undergraduate or graduate-level courses on computer science or engineering, as well as a guide for computer vision researchers, the book focuses on techniques that work under real-world large-dataset conditions.
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