Space-Frequency Shape Inference and Segmentation of 3D Surfaces
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Summary
It is shown how the space/frequency representation has the potential for unifying several different computer vision algorithms by showing how the spatial frequency shifts caused by perspective can be approximated by an &ne transformation which is a function of the textured surface’s surface normal.
- Type
- dissertation
- Published
- 2016-12-08
- Cited by
- 1
- References
- 121
- OpenAlex
- https://openalex.org/W88817261
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:115698417
Keywords
Artificial intelligence, Image texture, Computer vision, Texture filtering, Segmentation
References
- Moiré topography, sampling theory, and charged-coupled devices.
- Short-time Fourier transform
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- Sampled-grating and crossed-grating models of moire patterns from digital imaging
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- Shape from Texture
- The importance of phase in signals
- Texture discrimination by Gabor functions
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- Shading into Texture
- Radar Signals: An Introduction to Theory and Application
- Integrating sensor placement and visual tracking strategies
- Local Spatial Frequency Analysis for Computer Vision
- Terrain type recognition using ERTS-1 MSS images
- Finding natural clusters through entropy minimization
- A differential method for computing local shape-from-texture for planar and curved surfaces
- Sophisticated Signals and the Uncertainty Principle in Radar
- Shape-from-texture by wavelet-based measurement of local spectral moments
- Edge and Curve Detection for Visual Scene Analysis
- Wavelets and signal processing
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