A spatially constrained mixture model for image segmentation
By Konstantinos Blekas, Aristidis C. Likas, N. Galatsanos, Isaac E. Lagaris. 2005
233 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Sjoerd van Steenkiste
- J. Urgen Schmidhuber
- Klaus Greff
- Yoshua Bengio
- Sergey Levine
- Ajith Abraham
- Quoc V. Le
- Mohit Bansal
In Computer Vision and Pattern Recognition
- Sjoerd van Steenkiste
- J. Urgen Schmidhuber
- Klaus Greff
- Luc Van Gool
- Ming-Hsuan Yang
- Dacheng Tao
- Andrea Vedaldi
- Trevor Darrell
Citations in the corpus
- On the Binding Problem in Artificial Neural Networks (2020, background): “Mixture models can also be biased towards spatial coherence to explicitly account for the spatial structure of visual objects (Weiss and Adelson, 1996; Blekas et al., 2005).”
- The variational approximation for Bayesian inference (2008, methodology): “In general, the M-step for the MAP-EM algorithm is more complex than in its ML counterpart, see for example [30] and [31].”
- State-of-the-Art Methods for Brain Tissue Segmentation: A Review (2017, methodology): “[88] added spatial information in GMM using prior Gibbs distribution.”