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This book constitutes the refereed proceedings of the 12th International Symposium, ISICA 2021, held in Guangzhou, China, during November 19-21, 2021.  The 48 full papers included in this book were carefully reviewed and selected from 99 submissions. They were organized in topical sections as follows: new frontier of multi-objective evolutionary algorithms; intelligent multi-media; data modeling and application of artificial intelligence; exploration of novel intelligent optimization algorithm; and intelligent application of industrial production.

This book constitutes the post-conference proceedings of the 23rd International Conference on Data Analytics and Management in Data Intensive Domains, DAMDID/RCDL 2021, held in Moscow, Russia, in October 2021*.The 16 revised full papers were carefully reviewed and selected from 61 submissions. The papers are organized in the following topical sections: problem solving infrastructures, experiment organization, and machine learning applications; data analysis in astronomy; data analysis in material and earth sciences; information extraction from text* The conference was held virtually due to the COVID-19 pandemic.

Ultra-High Field Neuro MRI is a comprehensive reference and educational resource on the current state of neuroimaging at ultra-high field (UHF), with an emphasis on 7T. Sections cover the MR physics aspects of UHF, including the technical challenges and practical solutions that have enabled the rapid growth of 7T MRI. Individual chapters are dedicated to the different techniques that most strongly benefit from UHF, as well as chapters with a focus on different application areas in anatomical, functional and metabolic imaging. Finally, several chapters highlight the neurological and psychiatric applications for which 7T has shown benefits. The book is aimed at scientists who develop MR technologies and support clinical and neuroscience research, as well as users who want to benefit from UHF neuro MR techniques in their work. It also provides a comprehensive introduction to the field.

This book focuses on seven commonly used image analysis techniques. It covers aspects from basic principles and practical methods, to new advancement of each selected technique to help readers solve image processing related problems in real-life situation.

This book provides a description of designing and developing a computer assisted diagnosis (CAD) system based on thermography for diagnosing some of the common ailments such as arthritis, diabetes, and fever. It introduces applications of machine and deep learning methods, and convolutional neural networks in the development of CAD system.

This book constitutes the refereed proceedings of the 10th International Workshop on Biomedical Image Registration, WBIR 2020, which was supposed to be held in Munich, Germany, in July 2022.The 11 full and poster papers together with 17 short papers included in this volume were carefully reviewed and selected from 32 submitted papers. The papers are organized in the following topical sections: optimization, deep learning architectures, neuroimaging, diffeomorphisms, uncertainty, topology and metrics.

Diagnostic Biomedical Signal and Image Processing Applications with Deep Learning Methods presents comprehensive research on both medical imaging and medical signals analysis. The book discusses classification, segmentation, detection, tracking and retrieval applications of non-invasive methods such as EEG, ECG, EMG, MRI, fMRI, CT and X-RAY, amongst others. These image and signal modalities include real challenges that are the main themes that medical imaging and medical signal processing researchers focus on today. The book also emphasizes removing noise and specifying dataset key properties, with each chapter containing details of one of the medical imaging or medical signal modalities. Focusing on solving real medical problems using new deep learning and CNN approaches, this book will appeal to research scholars, graduate students, faculty members, R&D engineers, and biomedical engineers who want to learn how medical signals and images play an important role in the early diagnosis and treatment of diseases.

The papers in this volume were selected for presentation at the 16th Int- national Meshing Roundtable (IMR), held October 14-17, 2007 in Seattle, Washington, USA. The conference was started by Sandia National Labora- riesin1992asasmallmeetingoforganizationsstrivingtoestablishacommon focus for research and development in the ?eld of mesh generation. Now after 16 consecutive years, the International Meshing Roundtable has become r- ognized as an international focal point annually attended by researchers and developers from dozens of countries around the world. The 16th International Meshing Roundtable consists of technical pres- tations from contributed papers, keynote and invited talks, short course p- sentations, and a poster session and competition. The Program Committee would like to express its appreciation to all who participate to make the IMR a successful and enriching experience. The papers in these proceedings were selected from among 41 submissions by the Program Committee. Based on input from peer reviews, the committee selected these papers for their perceived quality, originality, and appropria- ness to the theme of the International Meshing Roundtable. We would like to thank all who submitted papers. We would also like to thank the colleagues who provided reviews of the submitted papers. The names of the reviewers are acknowledged in the following pages. We extend special thanks to Lynn Washburn, Bernadette Watts, and Jacqueline Finley for their time and e?ort to make the 16th IMR another outstanding conference.

Machine learning is a novel discipline concerned with the analysis of large and multiple variables data. It involves computationally intensive methods, like factor analysis, cluster analysis, and discriminant analysis. It is currently mainly the domain of computer scientists, and is already commonly used in social sciences, marketing research, operational research and applied sciences. It is virtually unused in clinical research. This is probably due to the traditional belief of clinicians in clinical trials where multiple variables are equally balanced by the randomization process and are not further taken into account. In contrast, modern computer data files often involve hundreds of variables like genes and other laboratory values, and computationally intensive methods are required. This book was written as a hand-hold presentation accessible to clinicians, and as a must-read publication for those new to the methods.

Scale is a concept the antiquity of which can hardly be traced. Certainly the familiar phenomena that accompany sc ale changes in optical patterns are mentioned in the earliest written records. The most obvious topological changes such as the creation or annihilation of details have been a topic to philosophers, artists and later scientists. This appears to of fascination be the case for all cultures from which extensive written records exist. For th instance, chinese 17 c artist manuals remark that "e;distant faces have no eyes"e; . The merging of details is also obvious to many authors, e. g. , Lucretius mentions the fact that distant islands look like a single one. The one topo- logical event that is (to the best of my knowledge) mentioned only late (by th John Ruskin in his "e;Elements of drawing"e; of the mid 19 c) is the splitting of a blob on blurring. The change of images on a gradual increase of resolu- tion has been a recurring theme in the arts (e. g. , the poetic description of the distant armada in Calderon's The Constant Prince) and this "e;mystery"e; (as Ruskin calls it) is constantly exploited by painters.

Motion-based recognition deals with the recognition of an object and/or its motion, based on motion in a series of images. In this approach, a sequence containing a large number of frames is used to extract motion information. The advantage is that a longer sequence leads to recognition of higher level motions, like walking or running, which consist of a complex and coordinated series of events. Unlike much previous research in motion, this approach does not require explicit reconstruction of shape from the images prior to recognition. This book provides the state-of-the-art in this rapidly developing discipline. It consists of a collection of invited chapters by leading researchers in the world covering various aspects of motion-based recognition including lipreading, gesture recognition, facial expression recognition, gait analysis, cyclic motion detection, and activity recognition. Audience: This volume will be of interest to researchers and post- graduate students whose work involves computer vision, robotics and image processing.

Invariant, or coordinate-free methods provide a natural framework for many geometric questions. Invariant Methods in Discrete and Computational Geometry provides a basic introduction to several aspects of invariant theory, including the supersymmetric algebra, the Grassmann-Cayler algebra, and Chow forms. It also presents a number of current research papers on invariant theory and its applications to problems in geometry, such as automated theorem proving and computer vision. Audience: Researchers studying mathematics, computers and robotics.