Name: Affine Analysis of Image Sequences (Distinguished Dissertations in Computer Science) (en Inglés)
Price: 72530.00 CLP
Availability: InStock
Author: Larry S. Shapiro
ISBN: 9780521019781

portada Affine Analysis of Image Sequences (Distinguished Dissertations in Computer Science) (en Inglés)

Formato

Libro Físico

Editorial

Cambridge University Press

Autor

Larry S. Shapiro

Año

2005

Idioma

Inglés

N° páginas

228

Encuadernación

Tapa Blanda

Peso

0.80

ISBN

0521019788

ISBN13

9780521019781

Categorías

Aplicaciones gráficas y multimedia

Affine Analysis of Image Sequences (Distinguished Dissertations in Computer Science) (en Inglés)

Larry S. Shapiro (Autor) · Cambridge University Press · Tapa Blanda

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Reseña del libro "Affine Analysis of Image Sequences (Distinguished Dissertations in Computer Science) (en Inglés)"

Computer vision is a rapidly growing field which aims to make computers 'see' as effectively as humans. In this book Dr Shapiro presents a new computer vision framework for interpreting time-varying imagery. This is an important task, since movement reveals valuable information about the environment. The fully-automated system operates on long, monocular image sequences containing multiple, independently-moving objects, and demonstrates the practical feasibility of recovering scene structure and motion in a bottom-up fashion. Real and synthetic examples are given throughout, with particular emphasis on image coding applications. Novel theory is derived in the context of the affine camera, a generalisation of the familiar scaled orthographic model. Analysis proceeds by tracking 'corner features' through successive frames and grouping the resulting trajectories into rigid objects using new clustering and outlier rejection techniques. The three-dimensional motion parameters are then computed via 'affine epipolar geometry', and 'affine structure' is used to generate alternative views of the object and fill in partial views. The use of all available features (over multiple frames) and the incorporation of statistical noise properties substantially improves existing algorithms, giving greater reliability and reduced noise sensitivity.