计算机视觉:一种现代方法(第二版) 英文版

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计算机视觉方向经典书目 Computer Vision: A Modern Approach MIT的经典教材。虽然已经过去十年了,还是值得一读
COMPUTER VISION AM○ DERN APPR○ACH SEC○ ND EDIT○N DAVID A.F○ RSYT University of Illinois at Urbana-Champaign JEAN P○NCE Ecole Normale Superieure PEARSON Boston Columbus Indianapolis New York San Francisco Upper Saddle River Amsterdam Cape Town Dubai London Madrid Milan Munich Paris Montreal Toronto Delhi Mexico City Sao Paulo Sydney Hong Kong Seoul Singapore Taipei Tokyo Vice president and editorial director ECS Senior Production Project Manager: Marilyn Lloyd Marcia horton Senior Operations Supervisor: Alan Fischer Editor in chiet. michael hirsch Operations specialist: Lisa McDowell Executive Editor: Tracy dunkelberger Art Director. Cover: Jayne conte Senior Project Manager: Carole Snyder Text Permissions: Dana Weightman/Rightshlouse Vice President Marketing: Patrice Jones Inc and Jen roach/PreMedia global Marketing Manager: Yez Alayan Cover lmage:⊙ Maxppp/L∪ MAPRESS cOn Marketing Coordinator: Kathryn Ferranti Media editor: Dan sandin Marketing assistant: Emma snider Composition: David Forsyth Vice president and director of production Printer/Binder: Edwards Brothers Ⅴ ince o' Brien Cover Printer: Lehigh-Phoenix Color Managing editor: Jeff Holcomb Credits and acknow ledgments borrowed from other sources and reproduced with permission, in this textbook appear on the appropriate page within text. Copyright o 2012, 2003 by Pearson Education, InC, publishing as Prentice Hall. All rights reserved Manufactured in the United States of America. This publication is protected by Copyright, and permission hould be obtained from the publisher prior to any prohibited reproduction, storage in a retrieval system,or transmission in any form or by any means electronic, mechanical, photocopying, recording, or likewise. To obtain permission(s) to use material from this work, please submit a written request to Pearson Education, Inc, Permissions Department, One Lake Street, Upper Saddle river, New Jersey 07458, or you may fax our request to 201-236-3290 Many of the designations by manufacturers and sellers to distinguish their products are claimed as trade marks. Where those designations appear in this book, and the publisher was aware of a trademark claim the designations have been printed in initial caps or all caps Library of Congress Cataloging-in-Publication Data available upon request 10987654321 PEARSON ISBN-l3:978-0-13-608592-8 ISBN-10:0-13-608592-X To my family-DAF To my father, Jean-Jacques Ponce--JP This page intentionally left blank Contents IMAGE FORMATION 1 Geometric camera models 1.1 Image上 ormation 1.1.1 Pinhole Perspective 1.1.2 Weak Perspective 1.1.3 Cameras with lenses 1.14 The human eⅤe TJ898 2 1.2 Intrinsic and extrinsic parameters 4 1.2.1 Rigid Transformations and homogeneous coordinates 14 1.2.2 Intrinsic parameters 1.2.3 Extrinsic Parameters .18 1.2.4 Perspective Projection Matrices 19 1. 2.5 Weak-Perspective Projection Matrices 1.3 Geometric Camera Calibration 1.3.1 A Linear Approach to Camera Calibration 1.3.2 A Nonlinear Approach to Camera Calibration 2 1.4 Note 2 Light and Shading 32 2.1 Modelling Pixel Brightness 32 2.1.1 Reflection at Surfaces 2.1.2 Sources and Their effects .34 2.1. 3 The Lambertian+Specular Model 2.1.4 Arca, Sources 36 2.2 Inference from Shading 37 2.2.1 Radiometric Calibration and High Dynamic Range Images 38 2.2. 2 The Shape of Specularities 40 2.2.3 Inferring lightness and illumination 43 2.2.4 Photometric STereo: Shape IroN Multiple Shaded Images . 46 2.3 Modelling InterreFlection 52 2.3.1 The Illumination at a Patch Due to an area source 52 2.3.2 Radiosity and Exitance .54 2.3.3 An Interreflection Model 55 2.3.4 Qualitative Properties of Interreflections 2.4 Shape from One Shaded Image 59 2. 5 Notes 3 Color 68 3.1 Human Color Perception 鲁鲁普 3.1.1 Color Matching 68 3.1.2 Color Receptors 3.2 The Physics of Color 3.2.1 The Color of Light Sources 3.2.2 The Color of Surfaces .76 3.3 Representing Color 3.3. 1 Linear Color Spaces 3.3.2 Non-linear Color Spac 3.4 A Model of Image Color 3.4.1 The Diffuse term 3.4.2 The Specular Term 3.5 Inference from Color 3.5. 1 Finding specularities Using color 3.5.2 Shadow Removal Using color 2 3.5. 3 Color Constancy: Surface Color Tronn Image Colon 9 3. 6 Notes I EARLY VISION: JUST ONE IMAGE 105 4 Linear filters l07 4.1 Linear Filters and Convolution 107 4.1.1 Convolution 107 4.2 Shift I t linear syste 4.2.1 Discrete Convolution 113 4.2.2 Continuous Convolution 115 4.2.3 Edge Effe 4.3 Spatial Frequency and Fourier Transforms 4.3.1 Fourier Transforms ..119 4.4 Sampling and Aliasing 121 4.4.1 Sampling 122 4.4.2 Aliasing 125 4.4.3 Smoothing and Resampling 126 4.5 Filters as Templates 131 4.5.1 Convolution as a Dot Product 4.5.2 Changing basis 132 4.6 Technique: Normalized Correlation and Finding Patterns 132 4.6. 1 Controlling the Television by Finding Hands by normalized Correlation 133 4.7 Tech P 134 4.7.1 The Gaussian Pyramid 135 4.7.2 Applications of Scaled Representations note 137 5 Local Image Features 141 5. 1 Computing the Iimage gradient 141 5.1.1 Derivative of Gaussian Filters 142 5.2 Representing the Image gradient .144 5.2.1 Gradient-Based Edge detectors 5.2.2 Orientations 147 5.3 Finding Corners and Building Neighborhoods l48 5.3.1 Finding corners 149 5.3.2 Using Scalc and Oricntation to Build a Ncighborhood 151 5.4 Describing Neighborhoods with SIFT and HOG Features 155 5.4.1 SIFT Features 157 5.4.2 HOG Fcaturcs 159 5.5 Computing Local Features in Practice 5.6 Noles 6 exture 164 6.1 Local Texture Representtions Using Filters 166 6.1.1 Spots and bars 167 6.1. 2 From Filter Outputs to Texture Representation 168 6.1.3 Local Texture Representations in Practice 170 6.2 Pooled Texture Representations by Discovering Textons 171 6.2.1 Vector Quantization and Textons 172 6.2.2 K-means Clustering for Vector Quantization 172 6.3 Synthesizing Textures and Filling holes in Images 6.3.1 Synthesis by Sampling Local Models ..176 6.3.2 Filling in Holes in Images 179 6.4 Image denoising 182 6.4.1 Non-local means 6.4.2 Block Matching 3D(BM3D) 183 6.4.3 Learned Sparse Codin 184 6.4.4 Results 186 6.5 Shape from Texture 187 6.5.1 Shape from Texture for Planes 187 6.5.2 Shape from Texture for Curved Surfaces 6.6 Notes 191 III EARLY VISION: MULTIPLE IMAGES 195 7 Stereopsis 197 7. 1 Binocular Camera geometry and the Epipolar Constraint 198 7.1.1 Epipolar geometry 7.1.2 The Essential matrix 200 7.1.3 The Fundamental matrix 201 7.2 Binocular reconstruction 201 7.2.1 Image Rectification 202 7.3 Hunan STereopsis 7. 4 Local methods for Binocular Fusion 205 7.4.1 Correlation 205 7.4.2 Multi-Scale Edge Matching 207 7.5 Global mcthods for Binocular fusion 210 7.5.1 Ordering Constraints and Dynamic Programming 210 7.5.2 Smoothness and graphs 211 7.6 Using morc cameras .214 7.7 Application: Robot Navigation ..215 7. 8 Noles · 216 8 Structurc from motion 221 8.1 Internally Calibrated Perspective Cameras 221 8.1.1 Natural Ambiguity of the Problem 8.1.2 Euclidean Structure and Motion from Two Images 224 8.1.3 Euclidean Structure and Motion from Multiple Images 8.2 Uncalibrated Weak-Perspective Cameras 8.2.1 Natural ambiguity of the Problem 8.2.2 Affine Structure and Motion from Two Images 233 8.2.3 Affine Structure and Motion from Multiple Images 237 8.2.4 From Affine to Euclidean Shape 238 8.3 Uncalibrated Perspective Cameras 8.3. 1 Natural ambiguity of the Problem 241 8.3. 2 Projective Structure and Motion from Two Images 242 8.3.3 Projective Structure and Motion from Multiple images 244 8.3.4 From Projective to Euclidean Shape 246 8. 4 Notes

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映影留心 正是我需要的英文版图像处理书籍。。。。。。。
2019-07-28
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