Analysis and Interpretation of Range Images

1 Report: 1988 NSF Range Image Understanding Workshop.- 1.1 Introduction.- 1.2 Issues in Sensing and Sensors.- 1.2.1 General Background.- 1.2.2 Popular 3D Range Sensors.- 1.2.3 Other 3D Sensing Techniques.- 1.2.4 Needs of Five Major Application Areas.- 1.2.5 Example: ERIM Range Sensor Specs..- 1.2.6 Status of Moire Technology..- 1.2.7 Commonly Cited Problems in Range Sensing.- 1.2.8 Future Efforts.- 1.3 Early Processing.- 1.3.1 Issues in Early Processing of Range Images.- 1.3.2 Definition of "Early" Processing.- 1.3.3 Surface Geometry.- 1.3.4 Early Processing Algorithms.- 1.3.5 Summary.- 1.4 Obejct Recognition.- 1.4.1 Matching.- 1.4.2 Modeling.- 1.5 Sensor Integration.- 1.6 Range Sensing for Navigation.- 1.6.1 System Parameters, and Navigational Tasks, and Representation.- 1.6.2 Case 1: An Underwater Surveyor.- 1.6.3 Case 2: Surveying an Urban Environment.- 1.7 Applications Group Report.- 1.8 Appendix.- 1.8.1 Overview Speakers.- 1.8.2 List of Participants.- 1.8.3 Workshop Groups and Group Chairs.- 2 A Rule-Based Approach to Binocular Stereopsis.- 2.1 Introduction..- 2.2 The MPG Approach to Binocular Fusion.- 2.2.1 Brief Review of the Coarse-to-Fine Matching Strategy.- 2.2.2 Some Computational Aspects of the MPG Algorithm.- 2.2.3 Problems With The MPG Approach.- 2.3 Review of Procedures for Stereo Matching Under High-level Constraints.- 2.3.1 Matching Using Geometrical Constraints.- 2.3.2 The Constraint on the Ordering of Features.- 2.3.3 Looser Ordering Constraint.- 2.3.4 Some Other Approaches.- 2.4 Matching Methods Included in the Rule-based Program.- 2.4.1 Dominant Feature Matching.- 2.4.2 Geometrically Constrained Matching.- 2.4.3 Matching of Zero-Crossing Contours.- 2.4.4 The Default Matcher.- 2.5 A Review of Some Important Rules.- 2.5.1 Overview of the Rule-Based Procedure.- 2.5.2 Some GROUP-1 Rules.- 2.6 Experimental Results.- 2.6.1 Experimental Setup.- 2.6.2 Stereo Images and Depth Maps.- 2.6.3 Comparison with the MPG Algorithm.- 2.7 Conclusions.- 3 Geometric Signal Processing.- 3.1 Introduction.- 3.2 Machine Perception.- 3.3 Geometric Representations.- 3.4 Geometric Sensors.- 3.5 Geometric Signal Modeling.- 3.5.1 Geometric Noise Modeling.- 3.6 Geometric Descriptions.- 3.6.1 Planar Curves.- 3.6.2 Space Curves.- 3.6.3 Surfaces.- 3.6.4 Volumes.- 3.6.5 Summary of Geometric Descriptions.- 3.7 Geometric Approximation.- 3.7.1 Local Approximation Methods.- 3.7.2 Global Approximation Methods.- 3.7.3 Function Approximation Comparisons.- 3.7.4 Other Methods of Interest.- 3.8 Robust Approximation.- 3.8.1 Robust M-Estimation.- 3.8.2 Basic Examples.- 3.9 Emerging Themes.- 4 Segmentation versus object representation - are they separable?.- 4.1 Introduction.- 4.2 The Role of Shape Primitives.- 4.3 Segmentation Process.- 4.3.1 Segmentation using volumetric representation.- 4.3.2 Segmentation using boundary information.- 4.3.3 Segmentation using surface primitives.- 4.4 Control Structure.- 4.5 Results.- 4.6 Summary.- 5 Object Recognition.- 5.1 Introduction.- 5.2 Aspects of the Object Recognition Problem.- 5.3 Recognition via Matching Sensed Data to Models.- 5.4 The Statistical Pattern Recognition Approach.- 5.4.1 Object as Feature Vector.- 5.4.2 The Pattern Recognition Paradigm.- 5.4.3 Piecewise Linear Decision Surfaces.- 5.4.4 k-Nearest Neighbors.- 5.4.5 Prototype matching.- 5.4.6 Sequential Decision-making.- 5.5 Object Represented as Geometric Aggregate.- 5.5.1 The Registration Paradigm.- 5.5.2 Pose Clustering Algorithm.- 5.5.3 Sequential Hypothesize and Test.- 5.5.4 Comparison of PC and H&T.- 5.6 Object as an Articulated Set of Parts.- 5.7 Concluding Discussion.- 6 Applications of Range Image Sensing and Processing.- 6.1 Introduction.- 6.2 Major Industrial Application Areas.- 6.2.1 Integrity and Placement Verification.- 6.2.2 Surface Inspection.- 6.2.3 Metrology.- 6.2.4 Guidance and Control.- 6.2.5 Modeling.- 6.3 Obstacles to Practical Application.- 6.3.1 Reflectance Dynamic Range.- 6.3.2 Surface Reflectance Artifacts.- 6.3.3 Secondary Reflections.- 6.3.4 Shadowing and Occlusion.- 6.3.5 Sensor Scanning and Transport.- 6.3.6 Surface Feature Extraction.- 6.4 Conclusion.- 7 3-D Vision Techniques for Autonomous Vehicles.- 7.1 Introduction.- 7.2 Active range and reflectance sensing.- 7.2.1 From range pixels to points in space.- 7.2.2 Reflectance images.- 7.2.3 Resolution and noise.- 7.3 Terrain representations.- 7.3.1 The elevation map as the data structure for terrain representation.- 7.3.2 Terrain representations and path planners.- 7.3.3 Low resolution: Obstacle map.- 7.3.4 Medium resolution: Polygonal terrain map.- 7.3.5 High resolution: Elevation maps for rough terrain.- 7.4 Combining multiple terrain maps.- 7.4.1 The terrain matching problem: iconic vs. feature-based.- 7.4.2 Feature-based matching.- 7.4.3 Iconic matching from elevation maps.- 7.5 Combining range and intensity data.- 7.5.1 The geometry of video cameras.- 7.5.2 The registration problem.- 7.5.3 Application to outdoor scene analysis.- 7.6 Conclusion.- 8 Multisensor Fusion for Automatic Scene Interpretation.- 8.1 Introduction.- 8.2 Image Models.- 8.2.1 Classes of Models.- 8.2.2 Some Examples of Image Models.- 8.3 Intersensory Verification of Image Features.- 8.3.1 Issues.- 8.3.2 Examples of Intersensory Verification of Image Features.- 8.4 Intersensory Verification from Physical Principles.- 8.4.1 Issues.- 8.4.2 Recent Work in Intersensory Analysis Using Physical Principles.- 8.5 Multisensory Vision - An Illustrative Example.- 8.6 Conclusions.