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Analytic Deformations of the Spectrum of a Family of Dirac Operators on an Odd-Dimensional Manifold with Boundary
The analytic perturbation theory for eigenvalues of Dirac operators on odd dimensional manifolds with boundary is described in terms of [italic]extended L2 eigenvectors [end italics] on manifolds with cylindrical ends. These are generalizations of the Atiyah-Patodi-Singer extended [italic capital]L2 kernel of a Dirac operator. We prove that they form a discrete set near zero and deform analytically, in contrast to [italic capital]L2 eigenvectors, which can be absorbed into the continuous spectrum under deformations when the tangential operator is not invertible. We show that the analytic deformation theory for extended [italic capital]L2 eigenvectors and Atiyah-Patodi-Singer eigenvectors coincides.
Functional and Shape Data Analysis
This textbook for courses on function data analysis and shape data analysis describes how to define, compare, and mathematically represent shapes, with a focus on statistical modeling and inference. It is aimed at graduate students in analysis in statistics, engineering, applied mathematics, neuroscience, biology, bioinformatics, and other related areas. The interdisciplinary nature of the broad range of ideas covered—from introductory theory to algorithmic implementations and some statistical case studies—is meant to familiarize graduate students with an array of tools that are relevant in developing computational solutions for shape and related analyses. These tools, gleaned from geome...
Reach for the Stars
Wrestling is as much a part of winter in Iowa as is snow and cold. Dreams of state championships begin in elementary school and, since 1972, come to fruition-or heartbreakingly fall short-at an arena in Des Moines in February or March. The tournament finals sell out, and individuals and teams carve their names on the sport's history tree each year. Some champions were deaf, some were amputees, but all earn the respect of thousands for their work ethic-a hallmark of the state's populace. Is this heaven? No, it's better than that. It's high school wrestling in Iowa!
Riemannian Computing in Computer Vision
This book presents a comprehensive treatise on Riemannian geometric computations and related statistical inferences in several computer vision problems. This edited volume includes chapter contributions from leading figures in the field of computer vision who are applying Riemannian geometric approaches in problems such as face recognition, activity recognition, object detection, biomedical image analysis, and structure-from-motion. Some of the mathematical entities that necessitate a geometric analysis include rotation matrices (e.g. in modeling camera motion), stick figures (e.g. for activity recognition), subspace comparisons (e.g. in face recognition), symmetric positive-definite matrices (e.g. in diffusion tensor imaging), and function-spaces (e.g. in studying shapes of closed contours).
Geometry of Group Representations
Contains papers based on talks delivered at the AMS-IMS-SIAM Summer Research Conference on the Geometry of Group Representations, held at the University of Colorado in Boulder in July 1987. This work offers an understanding of the state of research in the geometry of group representations and their applications.
Information Processing in Medical Imaging
This book constitutes the refereed proceedings of the 22nd International Conference on Information Processing in Medical Imaging, IPMI 2011, held at Kloster Irsee, Germany, in July 2011. The 24 full papers and 39 poster papers included in this volume were carefully reviewed and selected from 224 submissions. The papers are organized in topical sections on segmentation, statistical methods, shape analysis, registration, diffusion imaging, disease progression modeling, and computer aided diagnosis. The poster sessions deal with segmentation, shape analysis, statistical methods, image reconstruction, microscopic image analysis, computer aided diagnosis, diffusion imaging, functional brain analysis, registration and other related topics.
Riemannian Geometric Statistics in Medical Image Analysis
Over the past 15 years, there has been a growing need in the medical image computing community for principled methods to process nonlinear geometric data. Riemannian geometry has emerged as one of the most powerful mathematical and computational frameworks for analyzing such data. Riemannian Geometric Statistics in Medical Image Analysis is a complete reference on statistics on Riemannian manifolds and more general nonlinear spaces with applications in medical image analysis. It provides an introduction to the core methodology followed by a presentation of state-of-the-art methods. Beyond medical image computing, the methods described in this book may also apply to other domains such as sign...
Energy Minimization Methods in Computer Vision and Pattern Recognition
This book constitutes the refereed proceedings of the 6th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition held in Ezhou, China, in August 2007. Twenty-two full papers are presented along with fifteen poster papers. The papers are organized into topical sections on algorithms, applications, image parsing, image processing, motion, shape, and three-dimensional processing.
Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging
This handbook gathers together the state of the art on mathematical models and algorithms for imaging and vision. Its emphasis lies on rigorous mathematical methods, which represent the optimal solutions to a class of imaging and vision problems, and on effective algorithms, which are necessary for the methods to be translated to practical use in various applications. Viewing discrete images as data sampled from functional surfaces enables the use of advanced tools from calculus, functions and calculus of variations, and nonlinear optimization, and provides the basis of high-resolution imaging through geometry and variational models. Besides, optimization naturally connects traditional model...
Numerical Control: Part B
Numerical Control: Part B, Volume 24 in the Handbook of Numerical Analysis series, highlights new advances in the field, with this new volume presenting interesting chapters written by an international board of authors. Chapters in this volume include Control problems in the coefficients and the domain for linear elliptic equations, Computational approaches for extremal geometric eigenvalue problems, Non-overlapping domain decomposition in space and time for PDE-constrained optimal control problems on networks, Feedback Control of Time-dependent Nonlinear PDEs with Applications in Fluid Dynamics, Stabilization of the Navier-Stokes equations - Theoretical and numerical aspects, Reconstruction...
