he problem of analyzing sequences of images to extract three-dimensional T motion and structure has been at the heart of the research in computer vi sion for many years. It is very important since its success or failure will determine whether or not vision can be used as a sensory process in reactive systems. The considerable research interest in this field has been motivated at least by the following two points: 1. The redundancy of information contained in time-varying images can over come several difficulties encountered in interpreting a single image. 2. There are a lot of important…mehr
he problem of analyzing sequences of images to extract three-dimensional T motion and structure has been at the heart of the research in computer vi sion for many years. It is very important since its success or failure will determine whether or not vision can be used as a sensory process in reactive systems. The considerable research interest in this field has been motivated at least by the following two points: 1. The redundancy of information contained in time-varying images can over come several difficulties encountered in interpreting a single image. 2. There are a lot of important applications including automatic vehicle driv ing, traffic control, aerial surveillance, medical inspection and global model construction. However, there are many new problems which should be solved: how to effi ciently process the abundant information contained in time-varying images, how to model the change between images, how to model the uncertainty inherently associated with the imaging system and how to solve inverse problems which are generally ill-posed. There are of course many possibilities for attacking these problems and many more remain to be explored. We discuss a few of them in this book based on work carried out during the last five years in the Computer Vision and Robotics Group at INRIA (Institut National de Recherche en Informatique et en Automatique).Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
1. Introduction.- 1.1 Brief Overview of Motion Analysis.- 1.2 Statement of the "Motion from Stereo" Problem.- 1.3 Organization of The Book.- 2. Uncertainty Manipulation and Parameter Estimation.- 2.1 Probability Theory and Geometric Probability.- 2.2 Parameter Estimation.- 2.3 Summary.- 2.4 Appendix: Least-Squares Techniques.- 3. Reconstruction of 3D Line Segments.- 3.1 Why 3D Line Segments.- 3.2 Stereo Calibration.- 3.3 Algorithm of the Trinocular Stereovision.- 3.4 Reconstruction of 3D Segments.- 3.5 Summary.- 4. Representations of Geometric Objects.- 4.1 Rigid Motion.- 4.2 3D Line Segments.- 4.3 Summary.- 4.4 Appendix: Visualizing Uncertainty.- 5. A Comparative Study of 3D Motion Estimation.- 5.1 Problem Statement.- 5.2 Extended Kalman Filter Approaches.- 5.3 Minimization Techniques.- 5.4 Analytical Solution.- 5.5 Kim and Aggarwal's method.- 5.6 Experimental Results.- 5.7 Summary.- 5.8 Appendix: Motion putation Using the New Line Segment Representation.- 6. Matching and Rigidity Constraints.- 6.1 Matching as a Search.- 6.2 Rigidity Constraint.- 6.3 Completeness of the Rigidity Constraints.- 6.4 Error Measurements inn the Constraints.- 6.5 Other Formalisms Rigidity Constraints.- 6.6 Summary.- 7. Hypothesize-and-Verify Method for Two 3D View Motion Analysis.- 7.1 General Presentation.- 7.2 Generating Hypotheses.- 7.3 Verifying Hypothesis.- 7.4 Matching Noisy Segments.- 7.5 Experimental Results.- 7.6 Summary.- 7.7 Appendix: Transforming a 3D Line Segment.- 8. Further Considerations on Reducing Complexity.- 8.1 Sorting Data Features.- 8.2 "Good-Enough" Method.- 8.3 Speeding Up the Hypothesis Generation Process Through Grouping.- 8.4 Finding Clusters Based on Proximity.- 8.5 Finding Planes.- 8.6 Experimental Results.- 8.6.1 Grouping Results.- 8.6.2 MotionResults.- 8.7 Conclusion.- 9. Multiple Object Motions.- 9.1 Multiple Object Motions.- 9.2 Influence of Egomotion on Observed Object Motion.- 9.3 Experimental Results.- 9.4 Summary.- 10. Object Recognition and Localization.- 10.1 Model-Based Object Recognition.- 10.2 Adapting the Motion-Determination Algorithm.- 10.3 Experimental Result.- 10.4 Summary.- 11. Calibrating a Mobile Robot and Visual Navigation.- 11.1 The INRIA Mobile Robot.- 11.2 Calibration Problem.- 11.3 Navigation Problem.- 11.4 Experimental Results.- 11.5 Integrating Motion Information from Odometry.- 11.6 Summary.- 12. Fusing Multiple 3D Frames.- 12.1 System Description.- 12.2 Fusing Segments from Multiple Views.- 12.3 Experimental Results.- 12.4 Summary.- 13. Solving the Motion Tracking Problem: A Framework.- 13.1 Previous Work.- 13.2 Position of the Problem and Primary Ideas.- 13.3 Solving the Motion Tracking Problem: A Framework.- 13.4 Splitting or Merging.- 13.5 Handling Abrupt Changes of Motion.- 13.6 Discussion.- 13.7 Summary.- 14. Modeling and Estimating Motion Kinematics.- 14.1 The Classical Kinematic Model.- 14.2 Closed-Form Solutions for Some Special Motions.- 14.2.1 Motion with Constant Angular and Translational Velocities.- 14.2.2 Motion with Constant Angular Velocity and Constant Translational Acceleration.- 14.2.3 Motion with Constant Angular Velocity and General Translational Velocity.- 14.2.4 Discussions.- 14.3 Relation with Two-View Motion Analysis.- 14.4 Formulation for the EKF Approach.- 14.5 Linearized Kinematic Model.- 14.6 Summary.- 15. Implementation Details and Experimental Results.- 15.1 Matching Segments.- 15.2 Support of Existence.- 15.3 Algorithm of the Token Tracking Process.- 15.4 Grouping Tokens into Objects.- 15.5 Experimental Results.- 15.5.1 Synthetic Data.- 15.6 Summary.- 16. Conclusions and Perspectives.- 16.1 Summary.- 16.2 Perspectives.- Appendix: Vector Manipulation and Differentiation.- A.1 Manipulation of Vectors.- A.2 Differentiation of Vectors.- References.
1. Introduction.- 1.1 Brief Overview of Motion Analysis.- 1.2 Statement of the "Motion from Stereo" Problem.- 1.3 Organization of The Book.- 2. Uncertainty Manipulation and Parameter Estimation.- 2.1 Probability Theory and Geometric Probability.- 2.2 Parameter Estimation.- 2.3 Summary.- 2.4 Appendix: Least-Squares Techniques.- 3. Reconstruction of 3D Line Segments.- 3.1 Why 3D Line Segments.- 3.2 Stereo Calibration.- 3.3 Algorithm of the Trinocular Stereovision.- 3.4 Reconstruction of 3D Segments.- 3.5 Summary.- 4. Representations of Geometric Objects.- 4.1 Rigid Motion.- 4.2 3D Line Segments.- 4.3 Summary.- 4.4 Appendix: Visualizing Uncertainty.- 5. A Comparative Study of 3D Motion Estimation.- 5.1 Problem Statement.- 5.2 Extended Kalman Filter Approaches.- 5.3 Minimization Techniques.- 5.4 Analytical Solution.- 5.5 Kim and Aggarwal's method.- 5.6 Experimental Results.- 5.7 Summary.- 5.8 Appendix: Motion putation Using the New Line Segment Representation.- 6. Matching and Rigidity Constraints.- 6.1 Matching as a Search.- 6.2 Rigidity Constraint.- 6.3 Completeness of the Rigidity Constraints.- 6.4 Error Measurements inn the Constraints.- 6.5 Other Formalisms Rigidity Constraints.- 6.6 Summary.- 7. Hypothesize-and-Verify Method for Two 3D View Motion Analysis.- 7.1 General Presentation.- 7.2 Generating Hypotheses.- 7.3 Verifying Hypothesis.- 7.4 Matching Noisy Segments.- 7.5 Experimental Results.- 7.6 Summary.- 7.7 Appendix: Transforming a 3D Line Segment.- 8. Further Considerations on Reducing Complexity.- 8.1 Sorting Data Features.- 8.2 "Good-Enough" Method.- 8.3 Speeding Up the Hypothesis Generation Process Through Grouping.- 8.4 Finding Clusters Based on Proximity.- 8.5 Finding Planes.- 8.6 Experimental Results.- 8.6.1 Grouping Results.- 8.6.2 MotionResults.- 8.7 Conclusion.- 9. Multiple Object Motions.- 9.1 Multiple Object Motions.- 9.2 Influence of Egomotion on Observed Object Motion.- 9.3 Experimental Results.- 9.4 Summary.- 10. Object Recognition and Localization.- 10.1 Model-Based Object Recognition.- 10.2 Adapting the Motion-Determination Algorithm.- 10.3 Experimental Result.- 10.4 Summary.- 11. Calibrating a Mobile Robot and Visual Navigation.- 11.1 The INRIA Mobile Robot.- 11.2 Calibration Problem.- 11.3 Navigation Problem.- 11.4 Experimental Results.- 11.5 Integrating Motion Information from Odometry.- 11.6 Summary.- 12. Fusing Multiple 3D Frames.- 12.1 System Description.- 12.2 Fusing Segments from Multiple Views.- 12.3 Experimental Results.- 12.4 Summary.- 13. Solving the Motion Tracking Problem: A Framework.- 13.1 Previous Work.- 13.2 Position of the Problem and Primary Ideas.- 13.3 Solving the Motion Tracking Problem: A Framework.- 13.4 Splitting or Merging.- 13.5 Handling Abrupt Changes of Motion.- 13.6 Discussion.- 13.7 Summary.- 14. Modeling and Estimating Motion Kinematics.- 14.1 The Classical Kinematic Model.- 14.2 Closed-Form Solutions for Some Special Motions.- 14.2.1 Motion with Constant Angular and Translational Velocities.- 14.2.2 Motion with Constant Angular Velocity and Constant Translational Acceleration.- 14.2.3 Motion with Constant Angular Velocity and General Translational Velocity.- 14.2.4 Discussions.- 14.3 Relation with Two-View Motion Analysis.- 14.4 Formulation for the EKF Approach.- 14.5 Linearized Kinematic Model.- 14.6 Summary.- 15. Implementation Details and Experimental Results.- 15.1 Matching Segments.- 15.2 Support of Existence.- 15.3 Algorithm of the Token Tracking Process.- 15.4 Grouping Tokens into Objects.- 15.5 Experimental Results.- 15.5.1 Synthetic Data.- 15.6 Summary.- 16. Conclusions and Perspectives.- 16.1 Summary.- 16.2 Perspectives.- Appendix: Vector Manipulation and Differentiation.- A.1 Manipulation of Vectors.- A.2 Differentiation of Vectors.- References.
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