Graph-based representations and techniques for image processing and image analysis
By Alberto Sanfeliu, R. Alquézar, J. Andrade, J. Climent, F. Serratosa, J. Vergés-Llahí. 2002
98 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition, Graph Learning
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Michael M. Bronstein
- Federico Monti
- Emanuele Rodolà
- Davide Boscaini
- Jonathan Masci
- A. Bronstein
- Luc Van Gool
- Jan Svoboda
In Graph Learning
- Michael M. Bronstein
- Federico Monti
- Emanuele Rodolà
- Davide Boscaini
- Jan Svoboda
- Jonathan Masci
- Stephan Günnemann
- Aleksandar Bojchevski
Citations in the corpus
- Geometric Deep Learning on Graphs and Manifolds Using Mixture Model CNNs (2016, methodology): “ng, considering images as manifolds and employing tools from differential geometry for their processing and analysis. More recent graph-based image processing methods relying on spectral graph theory [46, 40, 61, 29] can be traced back to these works. Manifold learning. A similar trend of employing ”
- Tree2Vector: Learning a Vectorial Representation for Tree-Structured Data (2018, unknown): “image analysis [9].”
- Edge and line oriented contour detection: State of the art (2011, background): “account collinearity and co-circularity between pairs of adjacent edges [159–161].”