Algorithmic High-Dimensional Robust Statistics

Anzai, Y

Available

Liu, Haowei

<p><i>Emerging Trends in Image Processing, Computer Vision, and Pattern Recognition</i> discusses the latest in trends in imaging science which at its core consists of three intertwined computer science fields, namely: Image Processing, Computer Vision, and Pattern Recognition. There is significant renewed interest in each of these three fields fueled by Big Data and Data Analytic initiatives including but not limited to&#x3b; applications as diverse as computational biology, biometrics, biomedical imaging, robotics, security, and knowledge engineering. These three core topics discussed here provide a solid introduction to image processing along with low-level processing techniques<i>, </i>computer vision fundamentals along with examples of applied applications and pattern recognition algorithms and methodologies<b> </b>that will be of value to the image processing and computer vision research communities<i>.</i></p> <p>Drawing upon the knowledge of recognized experts with years of practical experience and discussing new and novel applications Editors' Leonidas Deligiannidis and Hamid Arabnia cover&#x3b; </p> <ul> <li>Many perspectives of image processing spanning from fundamental mathematical theory and sampling, to image representation and reconstruction, filtering in spatial and frequency domain, geometrical transformations, and image restoration and segmentation</li> <li>Key application techniques in computer vision some of which are camera networks and vision, image feature extraction, face and gesture recognition and biometric authentication</li> <li>Pattern recognition algorithms including but not limited to&#x3b; Supervised and unsupervised classification algorithms, Ensemble learning algorithms, and parsing algorithms.</li> <li>How to use image processing and visualization to analyze big data. </li></ul>

Ji, Rongrong

<p><i>Connectomics: Applications to Neuroimaging</i> is unique in presenting the frontier of neuro-applications using brain connectomics techniques. The book describes state-of-the-art research that applies brain connectivity analysis techniques to a broad range of neurological and psychiatric disorders (Alzheimer's, epilepsy, stroke, autism, Parkinson's, drug or alcohol addiction, depression, bipolar, and schizophrenia), brain fingerprint applications, speech-language assessments, and cognitive assessment.</p> <p>With this book the reader will learn: </p> <ul> <p> <li>Basic mathematical principles underlying connectomics</li> <li>How connectomics is applied to a wide range of neuro-applications</li> <li>What is the future direction of connectomics techniques.</li> </ul><b> </b> <p>This book is an ideal reference for researchers and graduate students in computer science, data science, computational neuroscience, computational physics, or mathematics who need to understand how computational models derived from brain connectivity data are being used in clinical applications, as well as neuroscientists and medical researchers wanting an overview of the technical methods.</p> <p>Features: </p> <ul> <p> <li>Combines connectomics methods with relevant and interesting neuro-applications</li> <li>Covers most of the hot topics in neuroscience and clinical areas</li> <li>Appeals to researchers in a wide range of disciplines: computer science, engineering, data science, mathematics, computational physics, computational neuroscience, as well as neuroscience, and medical researchers interested in the technical methods of connectomics</li></ul>