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Fast image segmentation based on boosted random forests, integral images, and features on demand

contributor authorKnauer, U.
contributor authorSeiffert, U.
date accessioned2020-03-12T22:35:05Z
date available2020-03-12T22:35:05Z
date issued2014
identifier other7015737.pdf
identifier urihttps://libsearch.um.ac.ir:443/fum/handle/fum/1087849?locale-attribute=en&show=full
formatgeneral
languageEnglish
publisherIEEE
titleFast image segmentation based on boosted random forests, integral images, and features on demand
typeConference Paper
contenttypeMetadata Only
identifier padid8225756
subject keywordsconvex programming
subject keywordsn invariance
subject keywordsn linear systems
subject keywordsn sparse matrices
subject keywordsn convex optimization algorithm
subject keywordsn dynamical structure functions
subject keywordsn linear network systems
subject keywordsn linear time-invariant system
subject keywordsn sparsity assumption
subject keywordsn structure reconstruction
subject keywordsn system matrix
subject keywordsn Convex functions
subject keywordsn Heuristic algorithms
subject keywordsn Linear systems
subject keywordsn Protocols
subject keywordsn Reconstruction algorithms
subject keywordsn Steady-state
subject keywordsn Transfer functions
subject keywordsn Convex optimization
subject keywordsn Dynamical structure function
identifier doi10.1109/ChiCC.2014.6895922
journal titleomputational Intelligence in Ensemble Learning (CIEL), 2014 IEEE Symposium on
filesize4459766
citations0


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