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

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

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Author: Larry S. Shapiro
Publisher: Cambridge University Press

Buy New: $39.99



New (13) Used (10) from $28.00


Media: Paperback
Pages: 224
Number Of Items: 1
Shipping Weight (lbs): 0.9
Dimensions (in): 9.5 x 6.7 x 0.6

ISBN: 0521019788
Dewey Decimal Number: 621.367
EAN: 9780521019781


Editorial Reviews:

Product Description
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.

Book Description
Computer vision is a rapidly growing field which aims to make computers 'see' as effectively as humans. In this book, based on his award-winning thesis, Dr Shapiro presents a new computer vision framework for interpreting time-varying imagery.

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