Abbey's Bookshop Logo
Go to my checkout basket
Login to Abbey's Bookshop
Register with Abbey's Bookshop
Gift Vouchers
Browse by Category

facebook
Google Book Preview
Computer Vision: A Modern Approach
— —
David A. Forsyth Jean Ponce
Computer Vision: A Modern Approach by David A. Forsyth at Abbey's Bookshop,

Computer Vision: A Modern Approach

David A. Forsyth Jean Ponce


9780136085928

Pearson


Computer vision


Paperback

800 pages

$232.95
We can order this in for you
How long will it take?
order qty:  
Add this item to my basket

Appropriate for upper-division undergraduate- and graduate-level courses in computer vision found in departments of Computer Science, Computer Engineering and Electrical Engineering.

This textbook provides the most complete treatment of modern computer vision methods by two of the leading authorities in the field. This accessible presentation gives both a general view of the entire computer vision enterprise and also offers sufficient detail for students to be able to build useful applications. Students will learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods.

By:   David A. Forsyth, Jean Ponce
Imprint:   Pearson
Country of Publication:   United States
Edition:   2nd edition
Dimensions:   Height: 254mm,  Width: 203mm,  Spine: 30mm
Weight:   1.400kg
ISBN:   9780136085928
ISBN 10:   013608592X
Pages:   800
Publication Date:   October 2011
Audience:   College/higher education ,  Primary
Replaced By:   9780273764144
Format:   Paperback
Publisher's Status:   Active

I IMAGE FORMATION 1 1 Geometric Camera Models 3 1.1 Image Formation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.1.1 Pinhole Perspective . . . . . . . . . . . . . . . . . . . . . . . 4 1.1.2 Weak Perspective . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.1.3 Cameras with Lenses . . . . . . . . . . . . . . . . . . . . . . . 8 1.1.4 The Human Eye . . . . . . . . . . . . . . . . . . . . . . . . . 12 1.2 Intrinsic and Extrinsic Parameters . . . . . . . . . . . . . . . . . . . 14 1.2.1 Rigid Transformations and Homogeneous Coordinates . . . . 14 1.2.2 Intrinsic Parameters . . . . . . . . . . . . . . . . . . . . . . . 16 1.2.3 Extrinsic Parameters . . . . . . . . . . . . . . . . . . . . . . . 18 1.2.4 Perspective Projection Matrices . . . . . . . . . . . . . . . . . 19 1.2.5 Weak-Perspective Projection Matrices . . . . . . . . . . . . . 20 1.3 Geometric Camera Calibration . . . . . . . . . . . . . . . . . . . . . 22 1.3.1 ALinear Approach to Camera Calibration . . . . . . . . . . . 23 1.3.2 ANonlinear Approach to Camera Calibration . . . . . . . . . 27 1.4 Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 2 Light and Shading 32 2.1 Modelling Pixel Brightness . . . . . . . . . . . . . . . . . . . . . . . 32 2.1.1 Reflection at Surfaces . . . . . . . . . . . . . . . . . . . . . . 33 2.1.2 Sources and Their Effects . . . . . . . . . . . . . . . . . . . . 34 2.1.3 The Lambertian+Specular Model . . . . . . . . . . . . . . . . 36 2.1.4 Area 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 from 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 . . . . . . . . . . . . 56 2.4 Shape from One Shaded Image . . . . . . . . . . . . . . . . . . . . . 59 2.5 Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 3 Color 68 3.1 Human Color Perception . . . . . . . . . . . . . . . . . . . . . . . . . 68 3.1.1 Color Matching . . . . . . . . . . . . . . . . . . . . . . . . . . 68 3.1.2 Color Receptors . . . . . . . . . . . . . . . . . . . . . . . . . 71 3.2 The Physics of Color . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 3.2.1 The Color of Light Sources . . . . . . . . . . . . . . . . . . . 73 3.2.2 The Color of Surfaces . . . . . . . . . . . . . . . . . . . . . . 76 3.3 Representing Color . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 3.3.1 Linear Color Spaces . . . . . . . . . . . . . . . . . . . . . . . 77 3.3.2 Non-linear Color Spaces . . . . . . . . . . . . . . . . . . . . . 83 3.4 AModel of Image Color . . . . . . . . . . . . . . . . . . . . . . . . . 86 3.4.1 The Diffuse Term . . . . . . . . . . . . . . . . . . . . . . . . . 88 3.4.2 The Specular Term . . . . . . . . . . . . . . . . . . . . . . . . 90 3.5 Inference from Color . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 3.5.1 Finding Specularities Using Color . . . . . . . . . . . . . . . 90 3.5.2 Shadow Removal Using Color . . . . . . . . . . . . . . . . . . 92 3.5.3 Color Constancy: Surface Color from Image Color . . . . . . 95 3.6 Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 II EARLY VISION: JUST ONE IMAGE 105 4 Linear Filters 107 4.1 Linear Filters and Convolution . . . . . . . . . . . . . . . . . . . . . 107 4.1.1 Convolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 4.2 Shift Invariant Linear Systems . . . . . . . . . . . . . . . . . . . . . 112 4.2.1 Discrete Convolution . . . . . . . . . . . . . . . . . . . . . . . 113 4.2.2 Continuous Convolution . . . . . . . . . . . . . . . . . . . . . 115 4.2.3 Edge Effects in Discrete Convolutions . . . . . . . . . . . . . 118 4.3 Spatial Frequency and Fourier Transforms . . . . . . . . . . . . . . . 118 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 . . . . . . . . . . . . . . . . . 131 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 Technique: Scale and Image Pyramids . . . . . . . . . . . . . . . . . 134 4.7.1 The Gaussian Pyramid . . . . . . . . . . . . . . . . . . . . . 135 4.7.2 Applications of Scaled Representations . . . . . . . . . . . . . 136 4.8 Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137 5 Local Image Features 141 5.1 Computing the Image Gradient . . . . . . . . . . . . . . . . . . . . . 141 5.1.1 Derivative of Gaussian Filters . . . . . . . . . . . . . . . . . . 142 5.2 Representing the Image Gradient . . . . . . . . . . . . . . . . . . . . 144 5.2.1 Gradient-Based Edge Detectors . . . . . . . . . . . . . . . . . 145 5.2.2 Orientations . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147 5.3 Finding Corners and Building Neighborhoods . . . . . . . . . . . . . 148 5.3.1 Finding Corners . . . . . . . . . . . . . . . . . . . . . . . . . 149 5.3.2 Using Scale and Orientation to Build a Neighborhood . . . . 151 5.4 Describing Neighborhoods with SIFT and HOG Features . . . . . . 155 5.4.1 SIFT Features . . . . . . . . . . . . . . . . . . . . . . . . . . 157 5.4.2 HOG Features . . . . . . . . . . . . . . . . . . . . . . . . . . 159 5.5 Computing Local Features in Practice . . . . . . . . . . . . . . . . . 160 5.6 Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 160 6 Texture 164 6.1 Local Texture Representations 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 . . . . . . . . . . 176 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 . . . . . . . . . . . . . . . . . . . . . . . . . 183 6.4.2 Block Matching 3D (BM3D) . . . . . . . . . . . . . . . . . . 183 6.4.3 Learned Sparse Coding . . . . . . . . . . . . . . . . . . . . . 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 . . . . . . . . . . . . 190 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 . . . . . . . . . . . . . . . . . . . . . . . . 198 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 Human Stereopsis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203 7.4 Local Methods for Binocular Fusion . . . . . . . . . . . . . . . . . . 205 7.4.1 Correlation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 205 7.4.2 Multi-Scale Edge Matching . . . . . . . . . . . . . . . . . . . 207 7.5 Global Methods for Binocular Fusion . . . . . . . . . . . . . . . . . . 210 7.5.1 Ordering Constraints and Dynamic Programming . . . . . . . 210 7.5.2 Smoothness and Graphs . . . . . . . . . . . . . . . . . . . . . 211 7.6 Using More Cameras . . . . . . . . . . . . . . . . . . . . . . . . . . . 214 7.7 Application: Robot Navigation . . . . . . . . . . . . . . . . . . . . . 215 7.8 Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 216 8 Structure from Motion 221 8.1 Internally Calibrated Perspective Cameras . . . . . . . . . . . . . . . 221 8.1.1 Natural Ambiguity of the Problem . . . . . . . . . . . . . . . 223 8.1.2 Euclidean Structure and Motion from Two Images . . . . . . 224 8.1.3 Euclidean Structure and Motion from Multiple Images . . . . 228 8.2 Uncalibrated Weak-Perspective Cameras . . . . . . . . . . . . . . . . 230 8.2.1 Natural Ambiguity of the Problem . . . . . . . . . . . . . . . 231 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 . . . . . . . . . . . . . . . . . . . 240 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 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 248 IV MID-LEVEL VISION 253 9 Segmentation by Clustering 255 9.1 Human Vision: Grouping and Gestalt . . . . . . . . . . . . . . . . . 256 9.2 Important Applications . . . . . . . . . . . . . . . . . . . . . . . . . 261 9.2.1 Background Subtraction . . . . . . . . . . . . . . . . . . . . . 261 9.2.2 Shot Boundary Detection . . . . . . . . . . . . . . . . . . . . 264 9.2.3 Interactive Segmentation . . . . . . . . . . . . . . . . . . . . 265 9.2.4 Forming Image Regions . . . . . . . . . . . . . . . . . . . . . 266 9.3 Image Segmentation by Clustering Pixels . . . . . . . . . . . . . . . 268 9.3.1 Basic Clustering Methods . . . . . . . . . . . . . . . . . . . . 269 9.3.2 The Watershed Algorithm . . . . . . . . . . . . . . . . . . . . 271 9.3.3 Segmentation Using K-means . . . . . . . . . . . . . . . . . . 272 9.3.4 Mean Shift: Finding Local Modes in Data . . . . . . . . . . . 273 9.3.5 Clustering and Segmentation with Mean Shift . . . . . . . . . 275 9.4 Segmentation, Clustering, and Graphs . . . . . . . . . . . . . . . . . 277 9.4.1 Terminology and Facts for Graphs . . . . . . . . . . . . . . . 277 9.4.2 Agglomerative Clustering with a Graph . . . . . . . . . . . . 279 9.4.3 Divisive Clustering with a Graph . . . . . . . . . . . . . . . . 281 9.4.4 Normalized Cuts . . . . . . . . . . . . . . . . . . . . . . . . . 284 9.5 Image Segmentation in Practice . . . . . . . . . . . . . . . . . . . . . 285 9.5.1 Evaluating Segmenters . . . . . . . . . . . . . . . . . . . . . . 286 9.6 Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 287 10 Grouping and Model Fitting 290 10.1 The Hough Transform . . . . . . . . . . . . . . . . . . . . . . . . . . 290 10.1.1 Fitting Lines with the Hough Transform . . . . . . . . . . . . 290 10.1.2 Using the Hough Transform . . . . . . . . . . . . . . . . . . . 292 10.2 Fitting Lines and Planes . . . . . . . . . . . . . . . . . . . . . . . . . 293 10.2.1 Fitting a Single Line . . . . . . . . . . . . . . . . . . . . . . . 294 10.2.2 Fitting Planes . . . . . . . . . . . . . . . . . . . . . . . . . . 295 10.2.3 Fitting Multiple Lines . . . . . . . . . . . . . . . . . . . . . . 296 10.3 Fitting Curved Structures . . . . . . . . . . . . . . . . . . . . . . . . 297 10.4 Robustness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 299 10.4.1 M-Estimators . . . . . . . . . . . . . . . . . . . . . . . . . . . 300 10.4.2 RANSAC: Searching for Good Points . . . . . . . . . . . . . 302 10.5 Fitting Using Probabilistic Models . . . . . . . . . . . . . . . . . . . 306 10.5.1 Missing Data Problems . . . . . . . . . . . . . . . . . . . . . 307 10.5.2 Mixture Models and Hidden Variables . . . . . . . . . . . . . 309 10.5.3 The EM Algorithm for Mixture Models . . . . . . . . . . . . 310 10.5.4 Difficulties with the EM Algorithm . . . . . . . . . . . . . . . 312 10.6 Motion Segmentation by Parameter Estimation . . . . . . . . . . . . 313 10.6.1 Optical Flow and Motion . . . . . . . . . . . . . . . . . . . . 315 10.6.2 Flow Models . . . . . . . . . . . . . . . . . . . . . . . . . . . 316 10.6.3 Motion Segmentation with Layers . . . . . . . . . . . . . . . 317 10.7 Model Selection: Which Model Is the Best Fit? . . . . . . . . . . . . 319 10.7.1 Model Selection Using Cross-Validation . . . . . . . . . . . . 322 10.8 Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 322 11 Tracking 326 11.1 Simple Tracking Strategies . . . . . . . . . . . . . . . . . . . . . . . . 327 11.1.1 Tracking by Detection . . . . . . . . . . . . . . . . . . . . . . 327 11.1.2 Tracking Translations by Matching . . . . . . . . . . . . . . . 330 11.1.3 Using Affine Transformations to Confirm a Match . . . . . . 332 11.2 Tracking Using Matching . . . . . . . . . . . . . . . . . . . . . . . . 334 11.2.1 Matching Summary Representations . . . . . . . . . . . . . . 335 11.2.2 Tracking Using Flow . . . . . . . . . . . . . . . . . . . . . . . 337 11.3 Tracking Linear Dynamical Models with Kalman Filters . . . . . . . 339 11.3.1 Linear Measurements and Linear Dynamics . . . . . . . . . . 340 11.3.2 The Kalman Filter . . . . . . . . . . . . . . . . . . . . . . . . 344 11.3.3 Forward-backward Smoothing . . . . . . . . . . . . . . . . . . 345 11.4 Data Association . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 349 11.4.1 Linking Kalman Filters with Detection Methods . . . . . . . 349 11.4.2 Key Methods of Data Association . . . . . . . . . . . . . . . 350 11.5 Particle Filtering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 350 11.5.1 Sampled Representations of Probability Distributions . . . . 351 11.5.2 The Simplest Particle Filter . . . . . . . . . . . . . . . . . . . 355 11.5.3 The Tracking Algorithm . . . . . . . . . . . . . . . . . . . . . 356 11.5.4 A Workable Particle Filter . . . . . . . . . . . . . . . . . . . . 358 11.5.5 Practical Issues in Particle Filters . . . . . . . . . . . . . . . 360 11.6 Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 362 V HIGH-LEVEL VISION 365 12 Registration 367 12.1 Registering Rigid Objects . . . . . . . . . . . . . . . . . . . . . . . . 368 12.1.1 Iterated Closest Points . . . . . . . . . . . . . . . . . . . . . . 368 12.1.2 Searching for Transformations via Correspondences . . . . . . 369 12.1.3 Application: Building Image Mosaics . . . . . . . . . . . . . . 370 12.2 Model-based Vision: Registering Rigid Objects with Projection . . . 375 12.2.1 Verification: Comparing Transformed and Rendered Source to Target . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 377 12.3 Registering Deformable Objects . . . . . . . . . . . . . . . . . . . . . 378 12.3.1 Deforming Texture with Active Appearance Models . . . . . 378 12.3.2 Active Appearance Models in Practice . . . . . . . . . . . . . 381 12.3.3 Application: Registration in Medical Imaging Systems . . . . 383 12.4 Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 388 13 Smooth Surfaces and Their Outlines 391 13.1 Elements of Differential Geometry . . . . . . . . . . . . . . . . . . . 393 13.1.1 Curves . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 393 13.1.2 Surfaces . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 397 13.2 Contour Geometry . . . . . . . . . . . . . . . . . . . . . . . . . . . . 402 13.2.1 The Occluding Contour and the Image Contour . . . . . . . . 402 13.2.2 The Cusps and Inflections of the Image Contour . . . . . . . 403 13.2.3 Koenderink's Theorem . . . . . . . . . . . . . . . . . . . . . . 404 13.3 Visual Events: More Differential Geometry . . . . . . . . . . . . . . 407 13.3.1 The Geometry of the Gauss Map . . . . . . . . . . . . . . . . 407 13.3.2 Asymptotic Curves . . . . . . . . . . . . . . . . . . . . . . . . 409 13.3.3 The Asymptotic Spherical Map . . . . . . . . . . . . . . . . . 410 13.3.4 Local Visual Events . . . . . . . . . . . . . . . . . . . . . . . 412 13.3.5 The Bitangent Ray Manifold . . . . . . . . . . . . . . . . . . 413 13.3.6 Multilocal Visual Events . . . . . . . . . . . . . . . . . . . . . 414 13.3.7 The Aspect Graph . . . . . . . . . . . . . . . . . . . . . . . . 416 13.4 Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 417 14 Range Data 422 14.1 Active Range Sensors . . . . . . . . . . . . . . . . . . . . . . . . . . 422 14.2 Range Data Segmentation . . . . . . . . . . . . . . . . . . . . . . . . 424 14.2.1 Elements of Analytical Differential Geometry . . . . . . . . . 424 14.2.2 Finding Step and Roof Edges in Range Images . . . . . . . . 426 14.2.3 Segmenting Range Images into Planar Regions . . . . . . . . 431 14.3 Range Image Registration and Model Acquisition . . . . . . . . . . . 432 14.3.1 Quaternions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 433 14.3.2 Registering Range Images . . . . . . . . . . . . . . . . . . . . 434 14.3.3 Fusing Multiple Range Images . . . . . . . . . . . . . . . . . 436 14.4 Object Recognition . . . . . . . . . . . . . . . . . . . . . . . . . . . . 438 14.4.1 Matching Using Interpretation Trees . . . . . . . . . . . . . . 438 14.4.2 Matching Free-Form Surfaces Using Spin Images . . . . . . . 441 14.5 Kinect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 446 14.5.1 Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 447 14.5.2 Technique: Decision Trees and Random Forests . . . . . . . . 448 14.5.3 Labeling Pixels . . . . . . . . . . . . . . . . . . . . . . . . . . 450 14.5.4 Computing Joint Positions . . . . . . . . . . . . . . . . . . . 453 14.6 Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 453 15 Learning to Classify 457 15.1 Classification, Error, and Loss . . . . . . . . . . . . . . . . . . . . . . 457 15.1.1 Using Loss to Determine Decisions . . . . . . . . . . . . . . . 457 15.1.2 Training Error, Test Error, and Overfitting . . . . . . . . . . 459 15.1.3 Regularization . . . . . . . . . . . . . . . . . . . . . . . . . . 460 15.1.4 Error Rate and Cross-Validation . . . . . . . . . . . . . . . . 463 15.1.5 Receiver Operating Curves . . . . . . . . . . . . . . . . . . . 465 15.2 Major Classification Strategies . . . . . . . . . . . . . . . . . . . . . 467 15.2.1 Example: Mahalanobis Distance . . . . . . . . . . . . . . . . 467 15.2.2 Example: Class-Conditional Histograms and Naive Bayes . . 468 15.2.3 Example: Classification Using Nearest Neighbors . . . . . . . 469 15.2.4 Example: The Linear Support Vector Machine . . . . . . . . 470 15.2.5 Example: Kernel Machines . . . . . . . . . . . . . . . . . . . 473 15.2.6 Example: Boosting and Adaboost . . . . . . . . . . . . . . . 475 15.3 Practical Methods for Building Classifiers . . . . . . . . . . . . . . . 475 15.3.1 Manipulating Training Data to Improve Performance . . . . . 477 15.3.2 Building Multi-Class Classifiers Out of Binary Classifiers . . 479 15.3.3 Solving for SVMS and Kernel Machines . . . . . . . . . . . . 480 15.4 Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 481 16 Classifying Images 482 16.1 Building Good Image Features . . . . . . . . . . . . . . . . . . . . . 482 16.1.1 Example Applications . . . . . . . . . . . . . . . . . . . . . . 482 16.1.2 Encoding Layout with GIST Features . . . . . . . . . . . . . 485 16.1.3 Summarizing Images with Visual Words . . . . . . . . . . . . 487 16.1.4 The Spatial Pyramid Kernel . . . . . . . . . . . . . . . . . . . 489 16.1.5 Dimension Reduction with Principal Components . . . . . . . 493 16.1.6 Dimension Reduction with Canonical Variates . . . . . . . . 494 16.1.7 Example Application: Identifying Explicit Images . . . . . . 498 16.1.8 Example Application: Classifying Materials . . . . . . . . . . 502 16.1.9 Example Application: Classifying Scenes . . . . . . . . . . . . 502 16.2 Classifying Images of Single Objects . . . . . . . . . . . . . . . . . . 504 16.2.1 Image Classification Strategies . . . . . . . . . . . . . . . . . 505 16.2.2 Evaluating Image Classification Systems . . . . . . . . . . . . 505 16.2.3 Fixed Sets of Classes . . . . . . . . . . . . . . . . . . . . . . . 508 16.2.4 Large Numbers of Classes . . . . . . . . . . . . . . . . . . . . 509 16.2.5 Flowers, Leaves, and Birds: Some Specialized Problems . . . 511 16.3 Image Classification in Practice . . . . . . . . . . . . . . . . . . . . . 512 16.3.1 Codes for Image Features . . . . . . . . . . . . . . . . . . . . 513 16.3.2 Image Classification Datasets . . . . . . . . . . . . . . . . . . 513 16.3.3 Dataset Bias . . . . . . . . . . . . . . . . . . . . . . . . . . . 515 16.3.4 Crowdsourcing Dataset Collection . . . . . . . . . . . . . . . 515 16.4 Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 517 17 Detecting Objects in Images 519 17.1 The Sliding Window Method . . . . . . . . . . . . . . . . . . . . . . 519 17.1.1 Face Detection . . . . . . . . . . . . . . . . . . . . . . . . . . 520 17.1.2 Detecting Humans . . . . . . . . . . . . . . . . . . . . . . . . 525 17.1.3 Detecting Boundaries . . . . . . . . . . . . . . . . . . . . . . 527 17.2 Detecting Deformable Objects . . . . . . . . . . . . . . . . . . . . . . 530 17.3 The State of the Art of Object Detection . . . . . . . . . . . . . . . 535 17.3.1 Datasets and Resources . . . . . . . . . . . . . . . . . . . . . 538 17.4 Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 539 18 Topics in Object Recognition 540 18.1 What Should Object Recognition Do? . . . . . . . . . . . . . . . . . 540 18.1.1 What Should an Object Recognition System Do? . . . . . . . 540 18.1.2 Current Strategies for Object Recognition . . . . . . . . . . . 542 18.1.3 What Is Categorization? . . . . . . . . . . . . . . . . . . . . . 542 18.1.4 Selection: What Should Be Described? . . . . . . . . . . . . . 544 18.2 Feature Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 544 18.2.1 Improving Current Image Features . . . . . . . . . . . . . . . 544 18.2.2 Other Kinds of Image Feature . . . . . . . . . . . . . . . . . . 546 18.3 Geometric Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . 547 18.4 Semantic Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 549 18.4.1 Attributes and the Unfamiliar . . . . . . . . . . . . . . . . . . 550 18.4.2 Parts, Poselets and Consistency . . . . . . . . . . . . . . . . . 551 18.4.3 Chunks of Meaning . . . . . . . . . . . . . . . . . . . . . . . . 554 VI APPLICATIONS AND TOPICS 557 19 Image-Based Modeling and Rendering 559 19.1 Visual Hulls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 559 19.1.1 Main Elements of the Visual Hull Model . . . . . . . . . . . . 561 19.1.2 Tracing Intersection Curves . . . . . . . . . . . . . . . . . . . 563 19.1.3 Clipping Intersection Curves . . . . . . . . . . . . . . . . . . 566 19.1.4 Triangulating Cone Strips . . . . . . . . . . . . . . . . . . . . 567 19.1.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 568 19.1.6 Going Further: Carved Visual Hulls . . . . . . . . . . . . . . 572 19.2 Patch-Based Multi-View Stereopsis . . . . . . . . . . . . . . . . . . . 573 19.2.1 Main Elements of the PMVS Model . . . . . . . . . . . . . . 575 19.2.2 Initial Feature Matching . . . . . . . . . . . . . . . . . . . . . 578 19.2.3 Expansion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 579 19.2.4 Filtering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 580 19.2.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 581 19.3 The Light Field . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 584 19.4 Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 587 20 Looking at People 590 20.1 HMM's, Dynamic Programming, and Tree-Structured Models . . . . 590 20.1.1 Hidden Markov Models . . . . . . . . . . . . . . . . . . . . . 590 20.1.2 Inference for an HMM . . . . . . . . . . . . . . . . . . . . . . 592 20.1.3 Fitting an HMM with EM . . . . . . . . . . . . . . . . . . . . 597 20.1.4 Tree-Structured Energy Models . . . . . . . . . . . . . . . . . 600 20.2 Parsing People in Images . . . . . . . . . . . . . . . . . . . . . . . . 602 20.2.1 Parsing with Pictorial Structure Models . . . . . . . . . . . . 602 20.2.2 Estimating the Appearance of Clothing . . . . . . . . . . . . 604 20.3 Tracking People . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 606 20.3.1 Why Human Tracking Is Hard . . . . . . . . . . . . . . . . . 606 20.3.2 Kinematic Tracking by Appearance . . . . . . . . . . . . . . . 608 20.3.3 Kinematic Human Tracking Using Templates . . . . . . . . . 609 20.4 3D from 2D: Lifting . . . . . . . . . . . . . . . . . . . . . . . . . . . 611 20.4.1 Reconstruction in an Orthographic View . . . . . . . . . . . . 611 20.4.2 Exploiting Appearance for Unambiguous Reconstructions . . 613 20.4.3 Exploiting Motion for Unambiguous Reconstructions . . . . . 615 20.5 Activity Recognition . . . . . . . . . . . . . . . . . . . . . . . . . . . 617 20.5.1 Background: Human Motion Data . . . . . . . . . . . . . . . 617 20.5.2 Body Configuration and Activity Recognition . . . . . . . . . 621 20.5.3 Recognizing Human Activities with Appearance Features . . 622 20.5.4 Recognizing Human Activities with Compositional Models . . 624 20.6 Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 624 20.7 Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 626 21 Image Search and Retrieval 627 21.1 The Application Context . . . . . . . . . . . . . . . . . . . . . . . . . 627 21.1.1 Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . 628 21.1.2 User Needs . . . . . . . . . . . . . . . . . . . . . . . . . . . . 629 21.1.3 Types of Image Query . . . . . . . . . . . . . . . . . . . . . . 630 21.1.4 What Users Do with Image Collections . . . . . . . . . . . . 631 21.2 Basic Technologies from Information Retrieval . . . . . . . . . . . . . 632 21.2.1 Word Counts . . . . . . . . . . . . . . . . . . . . . . . . . . . 632 21.2.2 Smoothing Word Counts . . . . . . . . . . . . . . . . . . . . . 633 21.2.3 Approximate Nearest Neighbors and Hashing . . . . . . . . . 634 21.2.4 Ranking Documents . . . . . . . . . . . . . . . . . . . . . . . 638 21.3 Images as Documents . . . . . . . . . . . . . . . . . . . . . . . . . . 639 21.3.1 Matching Without Quantization . . . . . . . . . . . . . . . . 640 21.3.2 Ranking Image Search Results . . . . . . . . . . . . . . . . . 641 21.3.3 Browsing and Layout . . . . . . . . . . . . . . . . . . . . . . 643 21.3.4 Laying Out Images for Browsing . . . . . . . . . . . . . . . . 644 21.4 Predicting Annotations for Pictures . . . . . . . . . . . . . . . . . . 645 21.4.1 Annotations from Nearby Words . . . . . . . . . . . . . . . . 646 21.4.2 Annotations from the Whole Image . . . . . . . . . . . . . . 646 21.4.3 Predicting Correlated Words with Classifiers . . . . . . . . . 648 21.4.4 Names and Faces . . . . . . . . . . . . . . . . . . . . . . . . 649 21.4.5 Generating Tags with Segments . . . . . . . . . . . . . . . . . 651 21.5 The State of the Art of Word Prediction . . . . . . . . . . . . . . . . 654 21.5.1 Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 655 21.5.2 Comparing Methods . . . . . . . . . . . . . . . . . . . . . . . 655 21.5.3 Open Problems . . . . . . . . . . . . . . . . . . . . . . . . . . 656 21.6 Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 659 VII BACKGROUND MATERIAL 661 22 Optimization Techniques 663 22.1 Linear Least-Squares Methods . . . . . . . . . . . . . . . . . . . . . . 663 22.1.1 Normal Equations and the Pseudoinverse . . . . . . . . . . . 664 22.1.2 Homogeneous Systems and Eigenvalue Problems . . . . . . . 665 22.1.3 Generalized Eigenvalues Problems . . . . . . . . . . . . . . . 666 22.1.4 An Example: Fitting a Line to Points in a Plane . . . . . . . 666 22.1.5 Singular Value Decomposition . . . . . . . . . . . . . . . . . . 667 22.2 Nonlinear Least-Squares Methods . . . . . . . . . . . . . . . . . . . . 669 22.2.1 Newton's Method: Square Systems of Nonlinear Equations. . 670 22.2.2 Newton's Method for Overconstrained Systems . . . . . . . . 670 22.2.3 The Gauss-Newton and Levenberg-Marquardt Algorithms . 671 22.3 Sparse Coding and Dictionary Learning . . . . . . . . . . . . . . . . 672 22.3.1 Sparse Coding . . . . . . . . . . . . . . . . . . . . . . . . . . 672 22.3.2 Dictionary Learning . . . . . . . . . . . . . . . . . . . . . . . 673 22.3.3 Supervised Dictionary Learning . . . . . . . . . . . . . . . . . 675 22.4 Min-Cut/Max-Flow Problems and Combinatorial Optimization . . . 675 22.4.1 Min-Cut Problems . . . . . . . . . . . . . . . . . . . . . . . . 676 22.4.2 Quadratic Pseudo-Boolean Functions . . . . . . . . . . . . . . 677 22.4.3 Generalization to Integer Variables . . . . . . . . . . . . . . . 679 22.5 Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 682 Bibliography 684 Index 737 List of Algorithms 760

David A. Forsyth received the D.Phil. degree in computer science from Oxford University. He is currently a Professor in the Computer Science Division at the University of California at Berkeley. He has co-authored over eighty technical papers on computer vision, computer graphics and machine learning and has co-edited two books. Jean Ponce received the Ph.D. degree in Computer Science from the University of Paris Orsay. He is currently a Professor in the Department of Computer Science and the Beckman Institute at the University of Illinois at Urbana Champaign. Professor Ponce has written over a hundred conference and journal papers and co-edited two books on a range of subjects including computer vision and robotics.

My Shopping Basket
Your cart does not contain any items.