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» On Randomized Lanczos Algorithms
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189
Voted
ICCV
2009
IEEE
16 years 12 months ago
Higher-Order Gradient Descent by Fusion-Move Graph Cut
Markov Random Field is now ubiquitous in many formulations of various vision problems. Recently, optimization of higher-order potentials became practical using higherorder graph...
Hiroshi Ishikawa
205
Voted
ECCV
2006
Springer
16 years 8 months ago
Smooth Image Segmentation by Nonparametric Bayesian Inference
A nonparametric Bayesian model for histogram clustering is proposed to automatically determine the number of segments when Markov Random Field constraints enforce smooth class assi...
Peter Orbanz, Joachim M. Buhmann
145
Voted
ICIP
1998
IEEE
16 years 8 months ago
Segmentation of Dermatoscopic Images by Stabilized Inverse Diffusion Equations
Several segmentation techniques were evaluated for their effectiveness in distinguishing lesion from background in dermatoscopic images of pigmented lesions (moles and melanomas)....
Jianbo Gao, Jun Zhang, Matthew G. Fleming, Ilya Po...
174
Voted
ICIP
1994
IEEE
16 years 8 months ago
A Tree Structured Bayesian Scalar Quantizer for Wavelet Based Image Compression
ABSTRACT the pyramid. Recently, a number of promising quanMultiresolution imagedecompositions (e. g., wavelets), in conjunction with a variety of quantization schemes, have been sh...
Birsen Yazici, Mary L. Comer, Rangasami L. Kashyap...
172
Voted
ICML
2006
IEEE
16 years 7 months ago
An analysis of graph cut size for transductive learning
I consider the setting of transductive learning of vertex labels in graphs, in which a graph with n vertices is sampled according to some unknown distribution; there is a true lab...
Steve Hanneke