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This book provides a view of low-rank and sparse computing, especially approximation, recovery, representation, scaling, coding, embedding and learning among unconstrained visual data.
The approach presented uses a local shape prior in each element of the underlying data domain and couples all local shape priors via smoothness constraints.
For instance, ifG is the 1 N group of projective transformations of E , then the property ofS being a circle is geometric forG but not forG , while the property of being a conic or a straight 0 1 line is geometric for bothG andG .
The papers are organized in topical sections on contrast-enhancing imaging, digital mammography... Læs mere
Das Buch wendet sich an Studenten Technischer Universitäten und Fachhochschulen und an Ingenieure in Forschung und Entwicklung, die sich in das Thema Bildübertragungstechnik einarbeiten wollen.
This book constitutes the proceedings of the 8th International Conference on Swarm... Læs mere
The papers are organized in topical sections on geometry, 2D and 3D shapes, 3D... Læs mere