Geometric Constraints for Object Detection and Delineation
The ability to extract generic 3D objects from images is a crucial step towards automation of a variety of problems in cartographic database compilation, industrial inspection and assembly, and autonomous navigation. Many of these problem domains do not have strong constraints on object shape or sce...
| Main Author: | |
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| Format: | eBook |
| Language: | English |
| Published: |
New York, NY
Springer US
2000, 2000
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| Edition: | 1st ed. 2000 |
| Series: | The Springer International Series in Engineering and Computer Science
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| Subjects: | |
| Online Access: | |
| Collection: | Springer Book Archives -2004 - Collection details see MPG.ReNa |
Table of Contents:
- 4.6 A summary of hypothesis generation
- 5. Combining and Verifying Primitives
- 5.1 Combining primitives in image space
- 5.2 Exploiting hypothesis lineage
- 5.3 From image space primitives to object space models
- 5.4 Primitive extension: extrusion methods
- 5.5 Hypothesis verification
- 5.6 General aspects of primitive manipulation and verification
- 6. Performance Evaluation and Analysis
- 6.1 Selecting evaluation metrics
- 6.2 Reference scene model compilation
- 6.3 Comparative performance evaluation methodology
- 6.4 Baseline performance results and comparative analysis
- 6.5 Image/scene complexity and its impact on performance
- 6.6 Detection and delineation performance case studies
- 6.7 Performance evaluation: conclusions
- 7. Conclusions
- 7.1 Research summary
- 7.2 Future research and applications
- Appendices
- A-Mathematical Tools
- A.1 Coordinate systems and transformations
- A.2 The Gaussian sphere
- A.3 Vanishing points
- A.4 Backprojection
- A.5 Finite image extent bias
- A.6 2D determinant tests
- B- Experimental Results
- References
- About the Author
- 1. Introduction
- 1.1 A survey of previous research
- 1.2 An approach for generic object detection and delineation
- 1.3 The role of geometry and structural cues
- 1.4 Main contributions of this book
- 2. Object Detection and Delineation
- 2.1 Modeling image geometry
- 2.2 Primitives: generic object models
- 2.3 Bounding hypothesis space
- 2.4 Modeling 3D effects
- 2.5 Evaluating performance
- 2.6 System structure
- 3. Primitives and Vanishing Points
- 3.1 Selecting primitives
- 3.2 Rectangular and triangular volumes
- 3.3 Previous methods for vanishing point detection
- 3.4 Primitive-based vanishing point detection
- 3.5 Edge error modeling
- 3.6 Performance evaluation and analysis
- 3.7 A summary of vanishing point analysis
- 4. Geometric Constraints for Hypothesis Generation
- 4.1 Corner detection
- 4.2 Corner constraints
- 4.3 2—corners
- 4.4 Performance evaluation of corner generation
- 4.5 Generating primitives from intermediate features