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ICIP
2003
IEEE
15 years 11 months ago
Image compression with on-line and off-line learning
Images typically contain smooth regions, which are easily compressed by linear transforms, and high activity regions (edges, textures), which are harder to compress. To compress t...
Patrice Y. Simard, Christopher J. C. Burges, David...
TMI
1998
122views more  TMI 1998»
14 years 9 months ago
Segmentation and Interpretation of MR Brain Images: An Improved Active Shape Model
Abstract— This paper reports a novel method for fully automated segmentation that is based on description of shape and its variation using point distribution models (PDM’s). An...
Nicolae Duta, Milan Sonka
ICCV
2009
IEEE
14 years 7 months ago
Landmark classification in large-scale image collections
With the rise of photo-sharing websites such as Facebook and Flickr has come dramatic growth in the number of photographs online. Recent research in object recognition has used su...
Yunpeng Li, David J. Crandall, Daniel P. Huttenloc...
IJCV
2007
157views more  IJCV 2007»
14 years 10 months ago
Recovering Surface Layout from an Image
Humans have an amazing ability to instantly grasp the overall 3D structure of a scene – ground orientation, relative positions of major landmarks, etc – even from a single ima...
Derek Hoiem, Alexei A. Efros, Martial Hebert
ICIP
2007
IEEE
15 years 4 months ago
Weighted Co-SVM for Image Retrieval with MVB Strategy
In relevance feedback, active learning is often used to alleviate the burden of labeling by selecting only the most informative data. Traditional data selection strategies often c...
Xiaoyu Zhang, Jian Cheng, Hanqing Lu, Songde Ma