A nonparametric statistical method for image segmentation using information theory and curve evolution

By Junmo Kim, John W. Fisher III, Anthony J. Yezzi Jr., Müjdat Çetin, Alan S. Willsky. IEEE Transactions on Image Processing, 2005

340 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/e96b7d584f36733e2fe5393e5929032a916db659

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

1. Meng Tang
2. Ismail Ben Ayed
3. John Philip Collomosse
4. Vittorio Ferrari
5. Dmitrii Marin
6. Bo Han
7. Tinghuai Wang
8. Jasper R. R. Uijlings

In Industrial and Manufacturing Engineering

1. Yuri Boykov

Citations in the corpus

• Segmentation of Natural Images by Texture and Boundary Compression (2010, neutral): “Several recent methods have been proposed to combine the cues of homogeneous color and texture with the cue of contours in the segmentation process, including Kim et al. (2005); Malik et al. (2001); Tu & Zhu (2002).”
• TouchCut: Fast image and video segmentation using single-touch interaction (2014, methodology): “We also fuse this boundary information with a region-based maximum a posteriori (MAP) criterion designed to promote color similarity with pixels local to the touched foreground point.”
• Secrets of GrabCut and Kernel K-Means (2015, background): “Thus, kernel K-means properties strongly depend on the bandwidth.”
• Image Segmentation Using Active Contours Driven by the Bhattacharyya Gradient Flow (2007, methodology): “divergence that could have been used instead of the Bhattacharyya coefficient to derive a level-set evolution [ 53 ].”