Riemannian Computing in Computer Vision (Hardcover)

Pavan K. Turaga

ISBN 10: 3319229567 / ISBN 13: 9783319229560

Editore: Springer International Publishing AG, Cham, 2015

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Hardcover. This book presents a comprehensive treatise on Riemannian geometric computations and related statistical inferences in several computer vision problems. This edited volume includes chapter contributions from leading figures in the field of computer vision who are applying Riemannian geometric approaches in problems such as face recognition, activity recognition, object detection, biomedical image analysis, and structure-from-motion. Some of the mathematical entities that necessitate a geometric analysis include rotation matrices (e.g. in modeling camera motion), stick figures (e.g. for activity recognition), subspace comparisons (e.g. in face recognition), symmetric positive-definite matrices (e.g. in diffusion tensor imaging), and function-spaces (e.g. in studying shapes of closed contours). This edited volume includes chapter contributions from leading figures in the field of computer vision who are applying Riemannian geometric approaches in problems such as face recognition, activity recognition, object detection, biomedical image analysis, and structure-from-motion. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Codice articolo 9783319229560

Dati bibliografici

• Titolo: Riemannian Computing in Computer Vision (Hardcover)
• Editore: Springer International Publishing AG, Cham
• Data di pubblicazione: 2015
• Lingua: Inglese
• ISBN 10: 3319229567
• ISBN 13: 9783319229560
• Rilegatura: Hardcover
• Edizione: prima edizione
• Condizione: new

Descrizione articolo

Riassunto

This book presents a comprehensive treatise on Riemannian geometric computations and related statistical inferences in several computer vision problems. This edited volume includes chapter contributions from leading figures in the field of computer vision who are applying Riemannian geometric approaches in problems such as face recognition, activity recognition, object detection, biomedical image analysis, and structure-from-motion. Some of the mathematical entities that necessitate a geometric analysis include rotation matrices (e.g. in modeling camera motion), stick figures (e.g. for activity recognition), subspace comparisons (e.g. in face recognition), symmetric positive-definite matrices (e.g. in diffusion tensor imaging), and function-spaces (e.g. in studying shapes of closed contours).

Informazioni sull?autore

Pavan Turaga is an Assistant Professor at Arizona State University Anuj Srivastava is a Professor at Florida State University