Computational analysis and learning for a biologically motivated model of boundary detection

By Iasonas Kokkinos, Rachid Deriche, Olivier D. Faugeras, Petros Maragos. 2008

17 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Biophysics, Cognitive Neuroscience, Computer Vision and Pattern Recognition, Neuroscience

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

1. Alan L. Yuille
2. Liang-Chieh Chen
3. Iasonas Kokkinos
4. George Papandreou
5. Kevin P. Murphy
6. Philip H. S. Torr
7. Quoc V. Le
8. Chunhua Shen

In Biophysics

1. Alan L. Yuille
2. Kevin P. Murphy
3. Iasonas Kokkinos
4. George Papandreou
5. Liang-Chieh Chen

In Cognitive Neuroscience

1. Iasonas Kokkinos
2. Alan L. Yuille
3. Kevin P. Murphy
4. Karl J. Friston

In Computer Vision and Pattern Recognition

1. Alan L. Yuille
2. Liang-Chieh Chen
3. Luc Van Gool
4. Iasonas Kokkinos
5. Ming-Hsuan Yang
6. Dacheng Tao
7. Xiaogang Wang
8. Chunhua Shen

In Neuroscience

1. Alan L. Yuille
2. Iasonas Kokkinos
3. George Papandreou

Citations in the corpus

• DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs (2016, background): “We note that mean field inference had been extensively studied for traditional image segmentation tasks [54], [55], [56], but these older models were typically limited to short-range connections.”
• Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs (2014, background): “…mean field had been extensively studied for traditional image segmentation/edge detection tasks, e.g ., (Geiger & Girosi, 1991; Geiger & Yuille, 1991; Kokkinos et al., 2008), but recently Kr ¨ ahenb ¨ uhl & Koltun (2011) showed that the inference can be very efficient for fully connected CRF and…”