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See below for a selection of the latest books from Image processing category. Presented with a red border are the Image processing books that have been lovingly read and reviewed by the experts at Lovereading. With expert reading recommendations made by people with a passion for books and some unique features Lovereading will help you find great Image processing books and those from many more genres to read that will keep you inspired and entertained. And it's all free!
Digital image processing is a progressive field that implements new techniques and methods continuously. The image processing application communities have developed increasingly reliable and consistent approaches for capturing data to meet a range of information needs. Statistically robust and transparent approaches for assessing accuracy and estimating area of change are critical to ensuring the integrity of digital image data information. Professionals need a reference source that tracks image processing advancements and how their practices are applied in the real world. The Handbook of Research on Recent Developments in Image Processing and Applications is a collection of innovative research on the latest advancements of bio-inspired computation and nature-motivated algorithms within imaging methods. While highlighting topics including graphic systems, pictorial databases, and 3D imaging, this book is ideally designed for image processing engineers, digital processing specialists, computer vision engineers, data scientists, industry professionals, researchers, academicians, and students seeking innovative avenues of research in the field of image processing.
Remote sensing of impervious surfaces has matured using advances in geospatial technology so recent that its applications have received only sporadic coverage in remote sensing literature. Remote Sensing of Impervious Surfaces is the first to focus entirely on this developing field. It provides detailed coverage of mapping, data extraction, and modeling techniques specific to analyzing impervious surfaces, such as roads and buildings. Written by renowned experts in the field, this book reviews the major approaches that apply to this emerging field as well as current challenges, developments, and trends. The authors introduce remote sensing digital image processing techniques for estimating and mapping impervious surfaces in urban and rural areas. Presenting the latest modeling tools and algorithms for data extraction and analysis, the book explains how to differentiate roads, roofs, and other manmade structures from remotely sensed images for individual analysis. The final chapters examine how to use impervious surface data for predicting the flow of storm- or floodwater and studying trends in population, land use, resource distribution, and other real-world applications in environmental, urban, and regional planning. Each chapter offers a consistent format including a concise review of basic concepts and methodologies, timely case studies, and guidance for solving problems and analyzing data using the techniques presented.
Human factors play a critical role in the design and interpretation of remotely sensed imagery for all Earth sciences. Remote Sensing and Cognition: Human Factors in Image Interpretation brings together current topics widely recognized and addressed regarding human cognition in geographic imagery, especially remote sensing imagery with complex data. It addresses themes around expertise including methods for knowledge elicitation and modeling of expertise, the effects of different aspects of realism on the interpretation of the environment, spatial learning using imagery, the effect of visual perspective on interpretation, and a variety of technologies and methods for utilizing knowledge in the analysis of remote sensing imagery. Written by leaders in the field, this book provides answers to the host of questions raised at the nexus of psychology and remote sensing. Academics and researchers with an interest in the human issues surrounding the use of remote sensing data will find this book to be an invaluable resource. The topics covered in this book are useful for both the scientific analysis of remote sensing imagery as well as the design and display of remote sensing imagery to facilitate a variety of other tasks including education and wayfinding. Features Brings together remote sensing, environmental, and computer scientists discussing their work from a psychological or human factors perspective Answers questions related to aesthetics of scientific visualization and mathematical analysis of perceptible objects Explains the perception and interpretation of realistic representations Provides illustrative real-world examples Shows how the features of display symbols, elements, and patterns have clear effects on processes of perception and visual search
Over the past decade, the field of image processing has made tremendous advances. One type of image processing that is currently of particular interest is tomographic imaging, a technique for computing the density function of a body, or discontinuity surfaces of this function. Today, tomography is widely used, and has applications in such fields as medicine, engineering, physics, geophysics, and security. The Radon Transform and Local Tomography clearly explains the theoretical, computational, and practical aspects of applied tomography. It includes sufficient background information to make it essentially self-contained for most readers.
This book presents a comprehensive review of heterogeneous face analysis and synthesis, ranging from the theoretical and technical foundations to various hot and emerging applications, such as cosmetic transfer, cross-spectral hallucination and face rotation. Deep generative models have been at the forefront of research on artificial intelligence in recent years and have enhanced many heterogeneous face analysis tasks. Not only has there been a constantly growing flow of related research papers, but there have also been substantial advances in real-world applications. Bringing these together, this book describes both the fundamentals and applications of heterogeneous face analysis and synthesis. Moreover, it discusses the strengths and weaknesses of related methods and outlines future trends. Offering a rich blend of theory and practice, the book represents a valuable resource for students, researchers and practitioners who need to construct face analysis systems with deep generative networks.
This two-volume set LNCS 12131 and LNCS 12132 constitutes the refereed proceedings of the 17th International Conference on Image Analysis and Recognition, ICIAR 2020, held in Povoa de Varzim, Portugal, in June 2020. The 54 full papers presented together with 15 short papers were carefully reviewed and selected from 123 submissions. The papers are organized in the following topical sections: image processing and analysis; video analysis; computer vision; 3D computer vision; machine learning; medical image and analysis; analysis of histopathology images; diagnosis and screening of ophthalmic diseases; and grand challenge on automatic lung cancer patient management. Due to the corona pandemic, ICIAR 2020 was held virtually only.
Vladislav Golyanik proposes several new methods for dense non-rigid structure from motion (NRSfM) as well as alignment of point clouds. The introduced methods improve the state of the art in various aspects, i.e. in the ability to handle inaccurate point tracks and 3D data with contaminations. NRSfM with shape priors obtained on-the-fly from several unoccluded frames of the sequence and the new gravitational class of methods for point set alignment represent the primary contributions of this book. About the Author: Vladislav Golyanik is currently a postdoctoral researcher at the Max Planck Institute for Informatics in Saarbrucken, Germany. The current focus of his research lies on 3D reconstruction and analysis of general deformable scenes, 3D reconstruction of human body and matching problems on point sets and graphs. He is interested in machine learning (both supervised and unsupervised), physics-based methods as well as new hardware and sensors for computer vision and graphics (e.g., quantum computers and event cameras).
This book constitutes the refereed proceedings of the 9th International Workshop on Biomedical Image Registration, WBIR 2020, which was supposed to be held in Portoroz, Slovenia, in June 2020. The conference was postponed until December 2020 due to the COVID-19 pandemic. The 16 full and poster papers included in this volume were carefully reviewed and selected from 22 submitted papers. The papers are organized in the following topical sections: Registration initialization and acceleration, interventional registration, landmark based registration, multi-channel registration, and sliding motion.
Rapid advances in silicon and other computer technologies are making high speed, parallel image processing computers a reality. These processors will increasingly be massively parallel and embedded in cameras and other sensors. As these devices proliferate, designers of image processing systems need to understand the basic algorithms and programming techniques for realizing solutions to problems in image analysis, vision, data compression, and information retrieval. This book covers key parallel architectures and algorithms for processing images, including both the theory of massively parallel mesh-based architectures and the practical aspects of programming these systems. The techniques presented have applications to computer vision, image compression, image analysis, and information retrieval. The book can be used as a course text for students in electrical engineering and computer science at the graduate and advanced undergraduate levels and as a reference for researchers and working professionals.
This book focuses on novel approaches for thoracic computed tomography (CT) image registration and determination of respiratory motion models in a range of patient scenarios. It discusses the use of image registration processes to remove the inconsistencies between medical images acquired using different devices. In the context of comparative research and medical analysis, these methods are of immense value in image registration procedures, not just for thoracic CT images, but for all types of medical images in multiple modalities, and also in establishing a mean respiration motion model. Combined with advanced techniques, the methods proposed have the potential to advance the field of computer vision and help improve existing methods. The book is a valuable resource for those in the scientific community involved in modeling respiratory motion for a large number of people.
Advances in Computational Techniques for Biomedical Image Analysis: Methods and Applications focuses on post-acquisition challenges such as image enhancement, detection of edges and objects, analysis of shape, quantification of texture and sharpness, and pattern analysis. It discusses the archiving and transfer of images, presents a selection of techniques for the enhancement of contrast and edges, for noise reduction and for edge-preserving smoothing. It examines various feature detection and segmentation techniques, together with methods for computing a registration or normalization transformation. Advances in Computational Techniques for Biomedical Image Analysis: Method and Applications is ideal for researchers and post graduate students developing systems and tools for health-care systems.
This easy-to-follow textbook presents an engaging introduction to the fascinating world of medical image analysis. Avoiding an overly mathematical treatment, the text focuses on intuitive explanations, illustrating the key algorithms and concepts in a way which will make sense to students from a broad range of different backgrounds. Topics and features: explains what light is, and how it can be captured by a camera and converted into an image, as well as how images can be compressed and stored; describes basic image manipulation methods for understanding and improving image quality, and a useful segmentation algorithm; reviews the basic image processing methods for segmenting or enhancing certain features in an image, with a focus on morphology methods for binary images; examines how to detect, describe, and recognize objects in an image, and how the nature of color can be used for segmenting objects; introduces a statistical method to determine what class of object the pixels in an image represent; describes how to change the geometry within an image, how to align two images so that they are as similar as possible, and how to detect lines and paths in images; provides further exercises and other supplementary material at an associated website. This concise and accessible textbook will be invaluable to undergraduate students of computer science, engineering, medicine, and any multi-disciplinary courses that combine topics on health with data science. Medical practitioners working with medical imaging devices will also appreciate this easy-to-understand explanation of the technology.