Optical Linear Feature Detection Based on Model Pose.
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Summary
A top-down approach is taken which searches an optical image for the locally optimal features based on the current hypothesized object pose, and the resulting linear features can then be matched against a CAD model.
- Type
- report
- Published
- 1995-12-16
- Cited by
- 7
- References
- 16
- Access
- Open access
- OpenAlex
- https://openalex.org/W1559883020
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:8500268
Keywords
Feature (linguistics), Artificial intelligence, Pattern recognition (psychology), Computer science, Computer vision
References
- Visual Object Recognition Using Deformable Models of Vehicles
- Coregistering 3D Models, Range, and Optical Imagery Using Least-Median Squares Fitting.
- Scale specific and robust edge/line encoding with linear combinations of Gabor wavelets
- The Design and Use of Steerable Filters
- Theory of edge detection
- The detection of intensity changes by computer and biological vision systems
- Authenticating Edges Produced by Zero-Crossing Algorithms
- A Computational Approach to Edge Detection
- Extracting Straight Lines
- Interleaving 3D model feature prediction and matching to support multi-sensor object recognition
- A parallel algorithm for polygon rasterization
- Edge Detection
- The Perceptual Organization of Visual Images: Segmentation as a Basis for Recognition,
Cited by
- Progress on Target and Terrain Recognition Research at Colorado State University.
- Integrated Color CCD and LADAR Based Object Modeling and Recognition: A Coregistration Approach to Multisensor Target Recognition with Extensions to Exploit Digital Elevation Map Data
- Image Analysis and Computer Vision: 1996
- Coregistration of range and optical images using coplanarity and orientation constraints
- Interleaving 3D model feature prediction and matching to support multi-sensor object recognition
- Augmented geophysical data interpretation through automated velocity picking in semblance velocity images
- Augmented geophysical data interpretation through automated velocity picking in semblance velocity images
- Automated Velocity Picking : A Computer Vision and Optimization Approach
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