Back to Results
First PageMeta Content
Bayesian statistics / Networks / Statistical models / Applied mathematics / Probability and statistics / Conditional random field / Boltzmann machine / Segmentation / Bayesian network / Graphical models / Statistics / Theoretical computer science


Augmenting CRFs with Boltzmann Machine Shape Priors for Image Labeling Andrew Kae∗1 , Kihyuk Sohn∗2 , Honglak Lee2 , Erik Learned-Miller1 1 University of Massachusetts, Amherst, MA, USA, {akae,elm}@cs.umass.edu 2 Uni
Add to Reading List

Document Date: 2013-04-21 18:03:25


Open Document

File Size: 4,43 MB

Share Result on Facebook

Company

MIT Press / AMD / Google / /

Country

Jordan / United States / /

/

Facility

University of Massachusetts / University of Michigan / /

IndustryTerm

region labeling applications / energy function / energy-based learning / Information processing / region growing algorithm / Parallel distributed processing / region labeling algorithms / convolutional deep belief networks / sigmoid belief networks / node energy function / energy functions / dynamical systems / learning algorithms / /

Organization

Perceptual Organization / University of Michigan / Ann Arbor / National Science Foundation / MIT / University of Massachusetts / Amherst / /

Person

Ariel Prabawa / E. Sharon / Jonathan Tiao / G. B. Huang / V / Predicting Structured / Sammy Hajalie / Jerod Weinman / E. Borenstein / S. Ullman / Flavio Fiszman / Gary Huang / Mark Schmidt / /

Position

appearance model for parts-based object segmentation / generative model for parts-based object segmentation / model global shapes / strong model for object shapes / model for hair segmentation / /

Product

Pentax K-x Digital Camera / /

ProvinceOrState

Massachusetts / Michigan / /

PublishedMedium

Journal of Artificial Intelligence Research / /

Technology

region labeling algorithms / DBMs / /

URL

http /

SocialTag