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Integrating Close-Range 3D Computer Vision and Synthetic Aperture Radar Satellite Remote Sensing
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Integrating Close-Range 3D Computer Vision
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- 1 Integrating Close-Range 3D Computer Vision and Synthetic Aperture Radar Satellite Remote Sensing
- 2 TUB Computer Vision & Remote Sensing Research Group
- 3 Medical Image Processing Projects
- 4 3D-Reconstruction Tool Chain
- 5 Discovering How Images Cover Object Graph of image triplets • Descriptor variance similarity to identify loop
- 6 OpenOF • Framework for sparse non-inear least squares optimization on a GPU
- 7 Image Measurements and knowledge: Camera Orientation Relative to Rectangle
- 8 Semantic Analysis • Various disciplines require semantic analysis of point clouds
- 9 Simplification - MDL Implies Trade-Off and fitting the measurements? . MDL principle: Transmitter wants to encode the information
- 10 Point Cloud Data Reduction Using MDL Principle
- 11 Segmentation into Low-Curvature and High-Curvature Segments
- 12 Voronoi-Based Extraction of a Feature Skeleton 11
- 13 Noise Influence on Point Cloud Segmentation
- 14 Performance Comparison of Point Cloud Segmentation Algorithms
- 15 Synthetic Aperture Radar (SAR) Remote Sensing
- 16 SAR Raw Data Processing Matched Filtering
- 17 Processing of SAR Data
- 18 Differential Interferometric SAR Phase Difference = f(Movement)
- 19 Compressive Sampling! Sparse Signal Representation Signal f can be transformed to a domain where it is sparse using an orthonormal basis eg a wavelet transform
- 20 Sparsity: Images in Wavelet Domain
- 21 Compressive Sampling II
- 22 Multibaseline SAR Acquisition
- 23 Proposal for Future Research: Use of Ubiquitous 3D Reconstructions
