Martin Burger – författare

This book constitutes the proceedings of the 7th International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2019, held in Hofgeismar, Germany, in June/July 2019. The 44 papers included in this volume were carefully reviewed and selected for inclusion in this book. They were organized in topical sections named: 3D vision and feature analysis; inpainting, interpolation and compression; inverse problems in imaging; optimization methods in imaging; PDEs and level-set methods; registration and reconstruction;  scale-space methods; segmentation and labeling; and variational methods.

This book offers a comprehensive exploration of the theoretical foundations of deep learning, bridging the gap between its ever increasing empirical success and rigorous mathematical understanding. With a focus on interdisciplinary approaches, it illuminates the interplay between mathematics, statistics, and computer science to address pressing challenges in deep learning research. While deep learning continues to revolutionize industries such as healthcare, gaming, and autonomous systems, its applications in solving mathematical problems like inverse problems and partial differential equations mark a paradigm shift in scientific inquiry. Yet, this rapid progress often outpaces our theoretical understanding, leaving critical questions about expressivity, optimization, robustness, and fairness unanswered.This book aims to close this gap by presenting perspectives from mathematics, statistics, and applications. This interdisciplinary endeavor draws upon diverse mathematical frameworks, including algebraic geometry, differential geometry, stochastics, functional analysis, and topology, alongside fundamental tools from statistics and theoretical computer science. By synthesizing these fields, the book not only advances the theoretical foundation of deep learning but also sets the stage for novel applications across scientific domains. Targeted at researchers, advanced students, and professionals in mathematics, computer science, and related fields, this book serves as both an introduction to emerging theoretical insights and a roadmap for interdisciplinary collaboration. It is will be of value to anyone seeking to understand or to contribute to the future of deep learning.

This book takes readers on a tour through modern methods in image analysis and reconstruction based on level set and PDE techniques, the major focus being on morphological and geometric structures in images.

The title of the book is a perfect way to synthesize the point of view of V- cenzo Capasso (VK) about the role played by mathematics in nature and all human activities, as mentioned by Willi J¨ ager in the Introduction. As VK mentioned in one of his speeches, “it is important that our scienti?c c- munity appreciates the importance of theory and mathematics to face the enormouschallengeswhicharisefromtheemerging?eldsofBiologyandMe- cine, Industry and Economics to improve the quality of Life. ” His enthusiasm and new ideas have brought him in this challenging mission. By transferring methods and tools from one ?eld to another, by learning novel mathem- ical methods and ideas from the observation of real world phenomena and behavior, and by approaching real industrial problems, he has accepted to play a fundamental role in both the Italian and European community in the establishment and reinforcement of communication channels between Ac- emia (methods) and Industry (applications), being himself a driving force of the scienti?c and technological progress that we are observing in our daily life. This books collects the proceedings of the workshop Math Everywhere: Deterministic and Stochastic Modelling in Biomedicine, Economics and - dustry which took place in Milano, Italy in the period September 4–6, 2005, and was organized in honor of the 60th birthday of Vincenzo Capasso (VK).