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of Symmetries and Repeated Patterns in 3D Point Cloud Data – Sylvain LAZARD (VEGAS, INRIA LORIA Nancy, France): 3D Visibility and Lines in Space VI Preface ´
Information theory has proved to be effective for solving many computer vision and pattern recognition (CVPR) problems (such as image matching, clustering and segmentation, saliency detection, feature selection, optimal classifier design and many others). Nowadays, researchers are widely bringing information theory elements to the CVPR arena. Among these elements there are measures (entropy, mutual information...), principles (maximum entropy, minimax entropy...) and theories (rate distortion theory, method of types...). This book explores and introduces the latter elements through an incremental complexity approach at the same time where CVPR problems are formulated and the most representative algorithms are presented. Interesting connections between information theory principles when applied to different problems are highlighted, seeking a comprehensive research roadmap. The result is a novel tool both for CVPR and machine learning researchers, and contributes to a cross-fertilization of both areas.
This book introduces the fundamentals of computer vision (CV), with a focus on extracting useful information from digital images and videos. Including a wealth of methods used in detecting and classifying image objects and their shapes, it is the first book to apply a trio of tools (computational geometry, topology and algorithms) in solving CV problems, shape tracking in image object recognition and detecting the repetition of shapes in single images and video frames. Computational geometry provides a visualization of topological structures such as neighborhoods of points embedded in images, while image topology supplies us with structures useful in the analysis and classification of image regions. Algorithms provide a practical, step-by-step means of viewing image structures. The implementations of CV methods in Matlab and Mathematica, classification of chapter problems with the symbols (easily solved) and (challenging) and its extensive glossary of key words, examples and connections with the fabric of CV make the book an invaluable resource for advanced undergraduate and first year graduate students in Engineering, Computer Science or Applied Mathematics. It offers insights into the design of CV experiments, inclusion of image processing methods in CV projects, as well as the reconstruction and interpretation of recorded natural scenes.
Covers both the fundamentals and the latest concepts in deep learning Presents some of the diverse applications of deep learning in visual computing and signal processing Includes over 90 figures and tables to elucidate the text
New Trends in Computer Graphics contains a selection of research papers submitted to Computer Graphics International '88 (COl '88). COl '88 is the Official Annual Conference of the Computer Graphics Society. Since 1982, this conference ha~ been held in Tokyo. This year, it is taking place in Geneva, Switzerland. In 1989, it will be held in Leeds, U. K. , in 1990 in Singapore, in 1991 in U. S. A. and in 1992 in Montreal, Canada. Over 100 papers were submitted to CGI '88 and 61 papers were selected by the International Program Committee. Papers have been grouped into 6 chapters. The flrst chapter is dedicated to Computer Animation because it deals with all topics presented in the other chapters. Several animation systems are described as well as speciflc subjects like 3D character animation, quaternions and splines. The second chapter is dedicated to papers on Image Synthesis, il1 particular new shading models and new algorithms for ray tracing are presented. Chapter 3 presents several algorithms for geometric modeling and new techniques for the creation and manipulation of curves, surfaces and solids and their applications to CAD. In Chapter 4, an important topic is presented: the specification of graphics systems and images using l~nguages and user-interfaces. The last two chapters are devoted to applications in sciences, medicine, engineering, art and business.
Computer Vision and Machine Intelligence for Renewable Energy Systems offers a practical, systemic guide to the use of computer vision as an innovative tool to support renewable energy integration.This book equips readers with a variety of essential tools and applications: Part I outlines the fundamentals of computer vision and its unique benefits in renewable energy system models compared to traditional machine intelligence: minimal computing power needs, speed, and accuracy even with partial data. Part II breaks down specific techniques, including those for predictive modeling, performance prediction, market models, and mitigation measures. Part III offers case studies and applications to a wide range of renewable energy sources, and finally the future possibilities of the technology are considered. The very first book in Elsevier's cutting-edge new series Advances in Intelligent Energy Systems, Computer Vision and Machine Intelligence for Renewable Energy Systems provides engineers and renewable energy researchers with a holistic, clear introduction to this promising strategy for control and reliability in renewable energy grids. - Provides a sorely needed primer on the opportunities of computer vision techniques for renewable energy systems - Builds knowledge and tools in a systematic manner, from fundamentals to advanced applications - Includes dedicated chapters with case studies and applications for each sustainable energy source
This book deals with computational anatomy, an emerging discipline recognized in medical science as a derivative of conventional anatomy. It is also a completely new research area on the boundaries of several sciences and technologies, such as medical imaging, computer vision, and applied mathematics. Computational Anatomy Based on Whole Body Imaging highlights the underlying principles, basic theories, and fundamental techniques in computational anatomy, which are derived from conventional anatomy, medical imaging, computer vision, and applied mathematics, in addition to various examples of applications in clinical data. The book will cover topics on the basics and applications of the new discipline. Drawing from areas in multidisciplinary fields, it provides comprehensive, integrated coverage of innovative approaches to computational anatomy. As well, Computational Anatomy Based on Whole Body Imaging serves as a valuable resource for researchers including graduate students in the field and a connection with the innovative approaches that are discussed. Each chapter has been supplemented with concrete examples of images and illustrations to facilitate understanding even for readers unfamiliar with computational anatomy.
"This book provides a comprehensive overview of machine learning research and technology in medical decision-making based on medical images"--Provided by publisher.
Modern factories are experiencing rapid digital transformation supported by emerging technologies, such as the Industrial Internet of things (IIOT), industrial big data and cloud technologies, deep learning and deep analytics, AI, intelligent robotics, cyber-physical systems and digital twins, complemented by visual computing (including new forms of artificial vision with machine learning, novel HMI, simulation, and visualization). This is evident in the global trend of Industry 4.0. The impact of these technologies is clear in the context of high-performance manufacturing. Important improvements can be achieved in productivity, systems reliability, quality verification, etc. Manufacturing processes, based on advanced mechanical principles, are enhanced by big data analytics on industrial sensor data. In current machine tools and systems, complex sensors gather useful data, which is captured, stored, and processed with edge, fog, or cloud computing. These processes improve with digital monitoring, visual data analytics, AI, and computer vision to achieve a more productive and reliable smart factory. New value chains are also emerging from these technological changes. This book addresses these topics, including contributions deployed in production, as well as general aspects of Industry 4.0.
This book focuses on the use of artificial intelligence to address a specific problem in the brain – the orientation distribution function. It discusses three aspects: (i) Preparing, enhancing and evaluating one of the cuckoo search algorithms (CSA); (ii) Describing the problem: Diffusion-weighted magnetic resonance imaging (DW-MRI) is used for non-invasive investigations of anatomical connectivity in the human brain, while Q-ball imaging (QBI) is a diffusion MRI reconstruction technique based on the orientation distribution function (ODF), which detects the dominant fiber orientations; however, ODF lacks local estimation accuracy along the path. (iii) Evaluating the performance of the CSA versions in solving the ODF problem using synthetic and real-world data. This book appeals to both postgraduates and researchers who are interested in the fields of medicine and computer science.