Three-Dimensional Computer Vision

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Morgan Francis
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$\bshiaki Shirai Three-Dimensional Computer Vision With 313$

1 \bshiaki Shirai Three-Dimensional Computer Vision With 313 Figures ' Springer-Verlag Berlin Heidelberg New York London Paris Tokyo

Table of Contents 1 Introduction 1 1.1 Three-Dimensional Computer Vision 1 1.2 Related Fields 3 1.2.1 Image Processing 3 1.2.2 Pattern Classification and Pattern Recognition 4 1.2.3 Computer Graphics 5 1.

2 Table of Contents 1 Introduction Three-Dimensional Computer Vision Related Fields Image Processing Pattern Classification and Pattern Recognition Computer Graphics Mainstream of 3D Computer Vision Research Pioneering Work First Generation Robot Vision Interpretation of Line Drawings Feature Extraction Range Data Processing Realizability of Line Drawings Use of Knowledge About Scenes Use of Physics of Imaging Marr's Theory of Human Vision and Computer Vision Image Input Imaging Geometry Image Input Devices Image Dissector Vidicon Solid Devices Color, Color Representation.. \ Color Input TV Signals Range Optical Time of Flight Ultrasonic Ranging Spot Projection Light-Stripe Method Moire Topography Preprocessing Noise Reduction Geometrical Correction Gray-Level Correction Correction of Defocusing 30

VIII Table of Contents 3 Image Feature Extraction 32 3.1 Edge Point Detection 32 3.1.1 Edge Types for a Polyhedral Image 32 3.1.2 One-Dimensional Edge Operators 33 3.1.3 Two-Dimensional Edge Operators 36 3.

3 VIII Table of Contents 3 Image Feature Extraction Edge Point Detection Edge Types for a Polyhedral Image One-Dimensional Edge Operators Two-Dimensional Edge Operators Pattern Matching Operations Color Edge Operators Determination of Edge Points Zero-Crossing Method Edge of a Curved Surface Local Edge Linking Roberts' Edge-Linking Method Edge Linking by Relaxation Edge Point Clustering in Parameter Space Hough Transformation Extension of Hough Transformation Edge-Following Methods Detection of Starting Point.... J Prediction of Next Edge Point Detection of Edge Point on Basis of Prediction Determination of Next Step Obtaining Connected Edge Points Region Methods Region Merging Region Splitting Region Splitting by Mode Methods Region Splitting Based on Discriminant Criterion 65 4 Image Feature Description Representation of Lines Spline Functions Smoothing Splines Parametric Splines B-Splines Segmentation of a Sequence of Points Approximation by Straight Lines Approximation by Curves Fitting Line Equations Using Errors Along a Single Axis Using Errors of Line Equations With Two Variables Using Distance From Each Point to Fitted Line Conversion Between Lines and Regions Boundary Detection Boundary Following Labeling Connected Regions 86

Table of Contents IX 5 Interpretation of Line Drawings 90 5.1 Roberts' Matching Method 90 5.2 Decomposition of Line Drawings Into Objects 93 5.3 Labeling Line Drawings 95 5.3.1 Vertex Type 95 5.3.2 Interpretation by Labeling 98 5.

4 Table of Contents IX 5 Interpretation of Line Drawings Roberts' Matching Method Decomposition of Line Drawings Into Objects Labeling Line Drawings Vertex Type Interpretation by Labeling Sequential Labeling Procedure Labeling by Relaxation Method Line Drawings with Shadows and Cracks Interpretation of Curved Objects Interpretation of Origami World Further Problems in Line Drawing Interpretation Realizability of Line Drawings Line Drawings Without Interpretations Use of Gradient Space Ill Gradient Space Ill Construction of Gradient Image Use of Linear Equation Systems Solving Linear Equation Systems Position-Free Line Drawings Realizability of Position-Constrained Line Drawings Stereo Vision Stereo Image Geometry Area-Based Stereo Feature Point Extraction Similarity Measures Finding Correspondence Multistage Matching Matching by Dynamic Programming Feature-Based Stereo Feature-Based Stereo for Simple Scenes Marr-Poggio-Grimson Algorithm Shape from Monocular Images Shape from Shading Reflectance Map Photometric Stereo Use of Surface Smoothness Constraint Use of Shading and Line Drawing Use of Polarized Light Shape from Geometrical Constraint on Scene Surface Orientation from Parallel Lines Shape from Texture Shape from Shape of Texture Elements Shape from Parallel Lines in Texture Shape from Parallel Lines Extracted from Texture 162

X Table of Contents 9 Range Data Processing 164 9.1 Range Data 164 9.2 Edge Point Detection Along a Stripe Image 165 9.2.1 One-Dimensional Jump Edge 165 9.2.2 One-Dimensional Discontinuous Edge 166 9.

5 X Table of Contents 9 Range Data Processing Range Data Edge Point Detection Along a Stripe Image One-Dimensional Jump Edge One-Dimensional Discontinuous Edge One-Dimensional Corner Edge Two-Dimensional Edge Operators for Range Images Two-Dimensional Jump Edge Two-Dimensional Discontinuous Edge Two-Dimensional Corner Edge Scene Segmentation Based on Stripe Image Analysis Segmentation of Stripe Image Construction of Planes Linking Three-Dimensional Edges Three-Dimensional Region Growing Outline of Region-Growing Method Construction of Surface Elements Merging Surface Elements Kernel Finding Region Merging Classification of Elementary Regions Merging Curved Elementary Regions Kernel Finding Region Merging Making Descriptions Fitting Quadratic Surfaces to Curved Regions Edges of Regions Properties of Regions and Relations Between Them Three-Dimensional Description and Representation Three-Dimensional Curves Three-Dimensional Curve Segments Three-Dimensional B-Splines Surfaces Coons Surface Patches B-Spline Surfaces Interpolation of Serial Sections with Surface Patches Description of Problem Determination of Initial Pair Selection of Next Vertex Generalized Cylinders Properties of Generalized Cylinders Describing Range Data by Generalized Cylinders Geometric Models Extended Gaussian Image 207

Table of Contents XI 11 Knowledge Representation and Use 209 11.1 Types of Knowledge 209 11.1.1 Knowledge About Scenes 209 11.1.2 Control 210 11.1.3 Bottom-Up Control 210 11.1.4 Top-Down Control 211 11.

6 Table of Contents XI 11 Knowledge Representation and Use Types of Knowledge Knowledge About Scenes Control Bottom-Up Control Top-Down Control Feedback Control Heterarchical Control Knowledge Representation Procedural and Declarative Representations Iconic Models Graph Models Demons Production Systems Blackboards Frames Image Analysis Using Knowledge About Scenes Analysis of Intensity Images Using Knowledge About Polyhedral Objects General Strategy Contour Finding Hypothesizing Lines Example of Line-Finding Procedure Verifying Hypothetical Line Segments Circular Search Extending Lines by Edge Following Experimental Results Analysis of Range Images with the Aid of a Junction Dictionary Possible Junctions Junction Dictionary System Organization Contour Finder Line-Segment Finder Edge Follower Straight-Line Fitter Body Partitioner Vertex-Position Adjuster Outline of Behavior of System Experimental Results Extension to Scenes with Curved Objects Image Understanding Using Two-Dimensional Models Recognition of Isolated Curved Objects Using a Graph Model Scene Description Evaluation of Matching Matching Strategy 251

XII Table of Contents 13.2 Interpretation of Imperfect Regions Using a Scene Model... 252 13.2.1 Scene Description 252 13.2.2 Relational Model of Scene 253 13.2.3 Interpretation by Relaxation Method 254 13.

7 XII Table of Contents 13.2 Interpretation of Imperfect Regions Using a Scene Model Scene Description Relational Model of Scene Interpretation by Relaxation Method Region Merging by Interpretation Recognition of Multiple Objects Using 2D Object Models Control Edge Finder and Description Maker Recognizer Total System Image Understanding Using Three-Dimensional Models Matching for Verification Vision Matching of Feature Points Matching of Features Without Finding Correspondence Matching Gray Images Synthesized from Surface Models Object Recognition by Predicting Image Features from Models Modeling Prediction Making Descriptions Interpretation Matching Geometric Models to the Description of a Single Object Recognition of Glossy Objects from Surface Normals Matching in the Extended Gaussian Image Recognition of Objects Using EGIs as Higher Level Models Recognition of Multiple Objects After Segmentation Recognition Without Segmentation Outline of Recognition Process Description of Scenes Kernel Selection Selecting the Principal Part of a Kernel Selecting the Subordinate Part of a Kernel Model Selection Kernel Consisting of Only the Principal Part Kernel Consisting of the Principal Part and the Subordinate Part Matching Between Regions Scene Interpretation 289 References 292

Image Processing, Analysis and Machine Vision

Image Processing, Analysis and Machine Vision Milan Sonka PhD University of Iowa Iowa City, USA Vaclav Hlavac PhD Czech Technical University Prague, Czech Republic and Roger Boyle DPhil, MBCS, CEng University