High-Speed Range Estimation Based on Intensity Gradient Analysis

20.12.1990

Englisch

9780387974798

Springer US

Springer Series in Perception Engineering

Nein

Kurt D. Skifstad

182

Gebundene Ausgabe

path planning, sensors, control, high-speed range estimation, calibration, sensor, uncertainty, machine vision, algorithms, intensity gradient analysis, stability

UMB - Algorithmen und Datenstrukturen UYT - Bildverarbeitung UYQV - Maschinelles Sehen, Bildverstehen UYQP - Mustererkennung TJFM - Regelungstechnik TJFD - Elektronische Geräte und Materialien

1 Introduction.- 1.1 Purpose.- 1.1.1 The Intensity Gradient Analysis Algorithm.- 1.1.2 Active Vision.- 1.2 Philosophy.- 1.3 The Structure of This Thesis.- 2 Approaches to the Depth Recovery Problem.- 2.1 Sensing Modalities.- 2.1.1 Active Modes of Sensing.- 2.1.2 Passive Modes of Sensing.- 2.2 Vision as a Primary Mode of Sensing.- 2.3 Literature Survey.- 2.3.1 Stereo.- 2.3.2 Camera Modeling.- 2.3.3 Image Matching: The Correspondence Problem.- 2.3.4 Feature Extraction.- 2.3.5 Computational Requirements.- 2.3.6 Optical Flow-Based Approaches.- 2.3.7 Gradient-Based Approaches.- 2.3.8 Depth From Focus.- 2.3.9 Structure (Shape) from “X”.- 2.3.10 Analysis of Spatio-temporal Solids.- 2.4 Summary.- 3 Depth Recovery.- 3.1 Depth Recovery Using Translational Sensor Motion.- 3.2 Special Case: Axial Camera Motion.- 3.3 Special Case: Lateral Camera Motion.- 3.4 The Parameters Needed for Depth Recovery.- 4 Theoretical Basis for IGA.- 4.1 Acquiring a Sequence of Images.- 4.2 Two Ideas and Their Implications.- 4.2.1 Induced Image Displacement.- 4.2.2 Temporal Coherence.- 4.2.3 Finding Disparity Without Solving Correspondence.- 4.2.4 Correlation vs. the “Opportunistic Procrastination” Approach.- 5 Intensity Gradient Analysis.- 5.1 Isolating Fixed Image Displacements Using Intensity Gradients.- 5.2 Why Do More Work?.- 5.3 Extending to Two Dimensions.- 5.4 The IGA Algorithm.- 5.4.1 The Initialization Stage.- 5.4.2 The Depth Recovery Loop.- 5.4.3 Computational Requirements.- 6 Implementation Issues.- 6.1 Problems with Real-World Sensors.- 6.1.1 Temporal Stability of the Imaging Sensor.- 6.1.2 Finite Spatial Resolution.- 6.1.3 Geometry of Conventional Imaging Arrays.- 6.2 Uncertainty in the Camera Motion Parameters.- 6.2.1 The “translation stage” scenario.- 6.2.2 The worst-case scenario.- 6.3 Moving Objects.- 6.4 Summary.- 7 Fixed Disparity Surfaces.- 7.1 Examples of FDS’s.- 7.2 Interpreting FDS’s.- 7.2.1 Example: Axial Camera Motion.- 7.2.2 Other Imaging Geometries.- 7.2.3 Selecting an Imaging Geometry.- 7.3 Fixed Disparity Surfaces and Conventional Stereo.- 8 Experiments.- 8.1 Experimental Setup.- 8.2 Calibration Procedures.- 8.2.1 Camera Alignment.- 8.2.2 Determination of Focal Length.- 8.3 Experiments.- 8.3.1 Smoothing Window Size.- 8.3.2 Threshold Value.- 8.3.3 Disparity.- 8.3.4 Camera Spacing.- 8.3.5 Orientation Angle.- 8.3.6 Lens Focal Length.- 8.3.7 Subsampling.- 8.4 Outdoor Scenes.- 8.5 Conclusions.- 9 An Application: Vision-Guided Navigation Using IGA.- 9.1 Navigation.- 9.2 Experimental Setup.- 9.2.1 The Platform.- 9.2.2 The World Model.- 9.2.3 Integrating Information.- 9.2.4 Path Planning.- 9.2.5 Camera Control Strategy.- 9.3 Experiments.- 9.3.1 Four Boxes Land.- 9.3.2 The Box Jungle.- 9.3.3 Five Box Land.- 9.3.4 Virtual Barrier Navigation.- 10 Conclusion.- 10.1 Future Research.- 10.1.1 Information Assimilation.- 10.1.2 The Construction of Dense Depth Maps.- 10.1.3 Multiple-sensor Implementation.- 10.1.4 VLSI Implementation.- 10.1.5 Moving Objects and Single-sensor IGA.- 10.2 Contribution.

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