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3-D Shape Estimation and Image Restoration: Exploiting Defocus and Motion-Blur

Autor Paolo Favaro, Stefano Soatto
en Limba Engleză Paperback – 18 feb 2013
Images contain information about the spatial properties of the scene they depict. When coupled with suitable assumptions, images can be used to infer thr- dimensional information. For instance, if the scene contains objects made with homogeneous material, such as marble, variations in image intensity can be - sociated with variations in shape, and hence the “shading” in the image can be exploited to infer the “shape” of the scene (shape from shading). Similarly, if the scene contains (statistically) regular structures, variations in image intensity can be used to infer shape (shape from textures). Shading, texture, cast shadows, - cluding boundaries are all “cues” that can be exploited to infer spatial properties of the scene from a single image, when the underlying assumptions are sat- ?ed. In addition, one can obtain spatial cues from multiple images of the same scene taken with changing conditions. For instance, changes in the image due to a moving light source are used in “photometric stereo,” changes in the image due to changes in the position of the cameras are used in “stereo,” “structure from motion,” and “motion blur. ” Finally, changes in the image due to changes in the geometry of the camera are used in “shape from defocus. ” In this book, we will concentrate on the latter two approaches, motion blur and defocus, which are referred to collectively as “accommodation cues.
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Specificații

ISBN-13: 9781849965590
ISBN-10: 1849965595
Pagini: 264
Ilustrații: XIV, 249 p.
Dimensiuni: 155 x 235 x 17 mm
Greutate: 0.37 kg
Ediția:2007
Editura: SPRINGER LONDON
Colecția Springer
Locul publicării:London, United Kingdom

Public țintă

Research

Cuprins

Basic models of image formation.- Some analysis: When can 3-D shape be reconstructed from blurred images?.- Least-squares shape from defocus.- Enforcing positivity: Shape from defocus and image restoration by minimizing I-divergence.- Defocus via diffusion: Modeling and reconstruction.- Dealing with motion: Unifying defocus and motion blur.- Dealing with multiple moving objects.- Dealing with occlusions.- Final remarks.

Recenzii

"This book presents a framework for estimating three-dimensional (3D) shapes from defocused and motion-blurred images. The book systematically describes various problems involved in estimating 3D shapes, and provides solutions to these problems… The book is well-written, and is equipped with Matlab code that implements the estimators presented in the chapters… I recommend this book to engineers in image processing and computer vision. Readers will learn state-of-the-art methods for shape restoration." (Hsun-Hsien Chang, ACM Computing Reviews, Vol. 49 (9), September 2008)

Textul de pe ultima copertă

Images contain information about the spatial properties of the scene they depict. When coupled with suitable assumptions, images can be used to infer three-dimensional information. This useful volume concentrates on motion blur and defocus, which can be exploited to infer the 3-D structure of a scene—as well as its radiance properties—and which in turn can be used to generate novel images with better quality.
3-D Shape Estimation and Image Restoration presents a coherent framework for the analysis and design of algorithms to estimate 3-D shape from defocused and motion blurred images, and to eliminate defocus and motion blur to yield "restored" images. It provides a collection of algorithms that are optimal with respect to the chosen model and estimation criterion.
Topics and Features include:
• Comprehensive introduction to guide readers through the different areas of the topic
• Basic models of image formation
• Discussion of least-squares shape from defocus
• Unifying defocus and motion blur
• Handling multiple moving objects
• Dealing with occlusions
• Appendices supply the necessary background in optimization and regularization
• www.eps.hw.ac.uk/~pf21/FavaroSoattoBook/downloads contains implementations of relevant algorithms, test data and demos.
Written for readers with interests in image processing and computer vision and with backgrounds in engineering, science or mathematics, this highly practical text/reference is accessible to advanced students or those with a degree that includes basic linear algebra and calculus courses. It can also be seen as a resource for practitioners looking to expand their knowledge in the subject.

Caracteristici

Describes analytical processes Delineates the options open to programmers Presents original algorithms Demonstrates a coherent analytical framework for the analysis and design of algorithms to estimate 3D shape from defocused and blurred images