Deep Structure, Singularities, and Computer Vision

Inhalt

Oral Presentations.- Blurred Correlation Versus Correlation Blur.- A Scale Invariant Covariance Structure on Jet Space.- Essential Loops and Their Relevance for Skeletons and Symmetry Sets.- Pre-symmetry Sets of 3D Shapes.- Deep Structure of Images in Populations Via Geometric Models in Populations.- Estimating the Statistics of Multi-object Anatomic Geometry Using Inter-object Relationships.- Histogram Statistics of Local Model-Relative Image Regions.- The Bessel Scale-Space.- Linear Image Reconstruction from a Sparse Set of ?-Scale Space Features by Means of Inner Products of Sobolev Type.- A Riemannian Framework for the Processing of Tensor-Valued Images.- From Stochastic Completion Fields to Tensor Voting.- Deep Structure from a Geometric Point of View.- Maximum Likely Scale Estimation.- Adaptive Trees and Pose Identification from External Contours of Polyhedra.- Poster Presentations.- Exploiting Deep Structure.- Scale-Space Hierarchy of Singularities.- Computing 3D Symmetry Sets; A Case Study.- Irradiation Orientation from Obliquely Viewed Texture.- Using Top-Points as Interest Points for Image Matching.- Transitions of Multi-scale Singularity Trees.- A Comparison of the Deep Structure of ?-Scale Spaces.- A Note on Local Morse Theory in Scale Space and Gaussian Deformations.