http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
Workshop on 3-D Machine Vision (1983 : Washington, D.C.)
National Conference on Artificial Intelligence (1983 : Washington, D.C.)
Sole distributors for the U.S.A. and Canada, Elsevier Science Pub. Co.
1 online resource (viii, 320 pages) : illustrations
Front Cover; Techniques for 3-D Machine Perception; Copyright Page; PREFACE; Table of Contents; CHAPTER 1. EXPERIMENTAL IMPLEMENTATION OF A RATIO IMAGE DEPTH SENSOR; I. Introduction; II. Principle of the Ratio Image Depth Sensor; III. The Experimental Implementation; IV. Analysis of Experimental Uncertainty; V. Representative Results; VI. Summary; References; CHAPTER 2. THE REPRESENTATION, RECOGNITION, AND POSITIONING OF 3-D SHAPES FROM RANGE DATA; 1. Introduction; 2. Representing 3-D shapes; 3. Recognition and positioning; 4. Conclusion; Acknowledgements; References
CHAPTER 3. STEREO VISION FOR THE ACQUISITION AND TRACKING OF MOVING THREE-DIMENSIONAL OBJECTS1. INTRODUCTION; 2. OVERVIEW OF ACQUISITION AND TRACKING; 3. APPROACH TO MOTION STEREO PROBLEM; 4. SCALE FACTOR AND BIAS; 5. MOTION STEREO SOLUTION; 6. COMPUTATION OF FEATURE POSITIONS AND UNCERTAINTIES; 7. EXAMPLE OF MOTION STEREO SOLUTION; ACKNOWLEDGMENTS; REFERENCES; CHAPTER 4. COMPUTING STEREOPSIS USING FEATURE POINT CONTOUR MATCHING; 1. Introduction; 2. The Marr-Poggio Stereo Model; 3. A Modified Marr-Poggio Stereo Matcher; 4. Examples; 5. Discussion; Acknowledgments; References
CHAPTER 5. MODEL-BASED RECOGNITION AND LOCALIZATION FROM SPARSE RANGE DATA1. The Problem and the Approach; 2. Generating Feasible Interpretations; 3. Model Testing; 4. Simulation Data; 5. Performance on Range Data; 6. Discussion; Acknowledgments; References; Appendix I; Appendix II; CHAPTER 6. REPRESENTATION AND INCREMENTAL CONSTRUCTION OF A THREE-DIMENSIONAL SCENE MODEL; 1. Introduction; 2. Description of System; 3. Representing and Manipulating the 3D Scene Model; 4. Modifications to the 3D Scene Model; 5. Constructing and Updating the 3D Scene Model; 6. Knowledge of Planar-Faced Objects
7. Knowledge of Urban Scenes8. Combining New Views with Current Model; 9. Summary; Acknowledgement; References; CHAPTER 7. KNOWLEDGE-BASED STEREO AND STRUCTURED LIGHT FOR 3-D ROBOT VISION; 1. INTRODUCTION; 2. SYSTEM OVERVIEW OF THE KNOWLEDGE-BASED VISION SYSTEM; 3. OBJECT RECOGNITION; 4. STEREOSCOPIC POSITION DETERMINATION; 5. RANGE MAPPING BY STRUCTURED LIGHT; 6. INTERPRETATION OF STRUCTURED-LIGHT RANGE DATA; 8. BIBLIOGRAPHICAL NOTES; 9. REFERENCES; CHAPTER 8. MODEL BASED INTERPRETATION OF 3-D RANGE DATA; INTRODUCTION; OBJECT MODELING; MODEL PREDICTION; 3-D FEATURE EXTRACTION
FEATURE TO MODEL MATCHINGCONCLUSIONS; REFERENCES; CHAPTER 9. MULTIPLE RESOLUTION SEARCH TECHNIQUES FOR THE HOUGH TRANSFORM IN HIGH DIMENSIONAL PARAMETER SPACES; 1. Introduction; 2. The Standard Hough Transform; 3. Two New Methods: Recursive Lattice Search and Resolution Hill Climbing; 4. Gradient Information; 5. More Abstract Problems: Recognizing Symmetries; 6. Results; 7. Conclusion; References; Appendices; CHAPTER 10. THE USE OF NUMERICAL RELATIONAL DISTANCE AND SYMBOLIC DIFFERENCES FOR ORGANIZING MODELS AND FOR MATCHING; I. Introduction; II. Relational Models and Relational Distance
Reconnaissance des formes (Informatique)
Machine intelligence and pattern recognition ; v. 3