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» Learning the Relative Importance of Features in Image Data
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NIPS
2007
13 years 10 months ago
Sparse Feature Learning for Deep Belief Networks
Unsupervised learning algorithms aim to discover the structure hidden in the data, and to learn representations that are more suitable as input to a supervised machine than the ra...
Marc'Aurelio Ranzato, Y-Lan Boureau, Yann LeCun
ICPR
2006
IEEE
14 years 9 months ago
Active Feature Models
In this paper active feature models are proposed. They utilize local texture features and a statistical shape model for the reliable localization of landmarks in images. They are ...
Georg Langs, Philipp Peloschek, Rene Donner, Micha...
ICPR
2010
IEEE
14 years 3 months ago
Typographical Features for Scene Text Recognition
Scene text images feature an abundance of font style variety but a dearth of data in any given query. Recognition methods must be robust to this variety or adapt to the query data...
Jerod Weinman
ISBI
2011
IEEE
13 years 11 days ago
Automatic pancreas segmentation in contrast enhanced CT data using learned spatial anatomy and texture descriptors
Pancreas segmentation in 3-D computed tomography (CT) data is of high clinical relevance, but extremely difficult since the pancreas is often not visibly distinguishable from the...
Marius Erdt, Matthias Kirschner, Klaus Drechsler, ...
ICIP
2000
IEEE
14 years 10 months ago
Objective Evaluation of Relative Segmentation Quality
When working in image and video segmentation, the major objective is to design an algorithm producing the appropriate segmentation results for the particular goals of the applicat...
Paulo Correia, Fernando Pereira