160,99 €
inkl. MwSt.
Versandkostenfrei*
Versandfertig in über 4 Wochen
  • Gebundenes Buch

"Computer Vision: From Surfaces to 3D Objects is the first book to take a full approach to the challenging issue of vertical 3D object representation. It introduces mathematical and conceptual advances that offer an unprecedented framework for analyzing the complex scene structure of the world. Leading theorists cover full 3D scene reconstruction, instead of the simplistic 2D planar algorithms employed in the past. They explore cutting-edge research on computational algorithms for scene analysis and present an integrated, complementary treatment of neural, behavioral, mathematical, and…mehr

Produktbeschreibung
"Computer Vision: From Surfaces to 3D Objects is the first book to take a full approach to the challenging issue of vertical 3D object representation. It introduces mathematical and conceptual advances that offer an unprecedented framework for analyzing the complex scene structure of the world. Leading theorists cover full 3D scene reconstruction, instead of the simplistic 2D planar algorithms employed in the past. They explore cutting-edge research on computational algorithms for scene analysis and present an integrated, complementary treatment of neural, behavioral, mathematical, and computational approaches. The text includes numerous graphics of complex processes, with many in color"--Provided by publisher.
Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Autorenporträt
Christopher W. Tyler is the director of the Brain Imaging Center at the Smith-Kettlewell Eye Research Institute. His current research encompasses brain imaging studies and mathematical modeling of the mechanisms of human stereoscopic depth, motion, and face perception as well as higher cognitive processing. He and his team have developed new methods to determine the dynamics of the neural population responses underlying brain imaging signals. By designing stimuli to probe specific neural sub-populations, this new methodology can be used to explore neural properties in the human brain and the changes in neural dynamics during the learning process.