Perspectives in Shape Analysis
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This book explores recent advancements in shape analysis, offering insights from experts in continuous-scale and discrete shape analysis, sparsity, and numerical computing. With the decreasing costs of high-resolution shape digitization, the challenge lies in analyzing vast amounts of data, especially for applications like human-computer interaction that require real-time processing. Addressing these challenges necessitates interdisciplinary approaches that integrate concepts from numerical computing, differential geometry, deformable shape modeling, sparse data representation, and machine learning. Many shape analysis tasks are modeled using partial differential equations, solvable through numerical computing techniques. Recent influences from differential geometry and deformable shape modeling are shaping new methods in this field. Additionally, sparse representation techniques aim to compress data, significantly reducing the volume to be processed. Machine learning also offers promising solutions. The Dagstuhl Seminar on New Perspectives in Shape Analysis, held in February 2014, aimed to tackle these challenges using cutting-edge geometric, algorithmic, and numerical tools, fostering collaboration among leading researchers to identify open problems and innovative solutions. This book will interest researchers in shape analysis and image and vision, appealing to both newcomers and seasoned experts eager to learn about the la




