no help available
no help available
userThe blue social bookmark and publication sharing system.
actions for all displayed bookmarks:
export:
sort:
- criterion: date, title
- order: ascending, descending
others:
- display: all, bookmarks only
- bookmarks per page: 5 10 20 50 100
bookmarks
actions for all displayed publications:
export:
sort:
- criterion: date, title
- order: ascending, descending
- advanced...
others:
- display: all, publications only
- publications per page: 5 10 20 50 100
publications (12)
- browse
- segmentation, as tag from all users
- related tags
- concepts
- tags
- imported
- image
- recognition,
- object
- Bayesian
- visual
- classification,
- detection,
- vision,
- extraction,
- learning
- computer
- networks,
- analysis,
- semantic
- feature
- network
- neural
- segmentation,
- context
- intelligence),
- learning,
- representation,
- retrieval,
- recognition
- (artificial
- nets,
- processing,
- systems,
- methods,
- probabilistic
- databases,
- pattern
- fuzzy
- categorization,
- models,
- system,
- theory,
- content-based
- knowledge
- global
- based
- information
- machine
- support
- multimedia
- model,
- reasoning,
- contextual
- human
- No matching items.
- Neural-network-based segmentation of multi-modal medical images: a comparative and prospective studyM. Ozkan, B.M. Dawant, and R.J. Maciunas. Medical Imaging, IEEE Transactions on 12(3):534--544 (1993)M. Ozkan, B.M. Dawant, and R.J. Maciunas. Medical Imaging, IEEE Transactions on 12(3):534--544 (1993)3 years and 9 months agoby mozaher1
- NMR,
- X-ray
- adaptive
- artificial
- biomedical
- brain,
- classical
- classification,
- classifier,
- computer
- computerised
- diagnostic
- generalization
- human
- image
- images
- images,
- imaging,
- intensity
- interslice
- learning
- likelihood
- magnetic
- maximum
- medical
- multimodal
- nets,
- networks,
- neural
- parameters
- parameters,
- processing,
- properties,
- resonance
- scheme,
- segmentation,
- tomography,
- topological
- variations,
NMR, X-ray adaptive artificial biomedical brain, classical classification, classifier, computer computerised diagnostic generalization human image images images, imaging, intensity interslice learning likelihood magnetic maximum medical multimodal nets, networks, neural parameters parameters, processing, properties, resonance scheme, segmentation, tomography, topological variations, - W. Osberger, and A.J. Maeder. Pattern Recognition, 1998. Proceedings. Fourteenth International Conference on, 1, page 701--704 vol.1. (1998)W. Osberger, and A.J. Maeder. Pattern Recognition, 1998. Proceedings. Fourteenth International Conference on, 1, page 701--704 vol.1. (1998)3 years and 9 months agoby mozaher1
- Y. LeCun, Fu Jie Huang, and L. Bottou. Computer Vision and Pattern Recognition, 2004. CVPR 2004. Proceedings of the 2004 IEEE Computer Society Conference on, 2, page II-97--104 Vol.2. (2004)Y. LeCun, Fu Jie Huang, and L. Bottou. Computer Vision and Pattern Recognition, 2004. CVPR 2004. Proceedings of the 2004 IEEE Computer Society Conference on, 2, page II-97--104 Vol.2. (2004)3 years and 9 months agoby mozaher2
- analysis,
- component
- computer
- convolutional
- databases,
- detection,
- error
- extraction,
- feature
- generic
- grayscale
- image
- images,
- invariance,
- large
- learning
- lighting
- low-resolution
- machines,
- methods,
- nearest
- neighbor
- networks,
- object
- pose
- principal
- processing,
- rates
- recognition,
- segmentation,
- stereo
- support
- test
- vector
- very
- vision,
- visual
analysis, component computer convolutional databases, detection, error extraction, feature generic grayscale image images, invariance, large learning lighting low-resolution machines, methods, nearest neighbor networks, object pose principal processing, rates recognition, segmentation, stereo support test vector very vision, visual - Sanjiv Kumar, and M. Hebert. Computer Vision, 2003. Proceedings. Ninth IEEE International Conference on, page 1150--1157 vol.2. (2003)Sanjiv Kumar, and M. Hebert. Computer Vision, 2003. Proceedings. Ninth IEEE International Conference on, page 1150--1157 vol.2. (2003)3 years and 9 months agoby mozaher2
- Bayes
- Corel
- Ising
- Markov
- based
- classification,
- classifiers,
- computer
- conditional
- constraints,
- contextual
- database,
- detection,
- discriminative
- field,
- fields,
- framework,
- generalized
- generative
- hidden
- hierarchical
- image
- images,
- interaction,
- interactions,
- kernel
- labeling,
- likelihood
- linear
- local
- man-made
- marginal
- maximum
- model
- model,
- models,
- natural
- neighborhood
- parameters,
- posterior
- posterior,
- probabilistic
- processes,
- random
- regions,
- rule,
- segmentation,
- sequences
- solution,
- structure
- text
- texture
- vision,
Bayes Corel Ising Markov based classification, classifiers, computer conditional constraints, contextual database, detection, discriminative field, fields, framework, generalized generative hidden hierarchical image images, interaction, interactions, kernel labeling, likelihood linear local man-made marginal maximum model model, models, natural neighborhood parameters, posterior posterior, probabilistic processes, random regions, rule, segmentation, sequences solution, structure text texture vision, - A. Ghanei, H. Soltanian-Zadeh, and J.P. Windham. Image Processing, 1996. Proceedings., International Conference on (September 1996)A. Ghanei, H. Soltanian-Zadeh, and J.P. Windham. Image Processing, 1996. Proceedings., International Conference on (September 1996)3 years and 9 months agoby mozaher2
- V. Ferrari, L. Fevrier, F. Jurie, and C. Schmid. Pattern Analysis and Machine Intelligence, IEEE Transactions on 30(1):36-51 (2008)V. Ferrari, L. Fevrier, F. Jurie, and C. Schmid. Pattern Analysis and Machine Intelligence, IEEE Transactions on 30(1):36-51 (2008)3 years and 9 months agoby mozaher2
- M.E. Farmer, and A.K. Jain. Image Processing, IEEE Transactions on 14(12):2060-2072 (December 2005)M.E. Farmer, and A.K. Jain. Image Processing, IEEE Transactions on 14(12):2060-2072 (December 2005)3 years and 9 months agoby mozaher2
- T. Blaschke. Advances in Techniques for Analysis of Remotely Sensed Data, 2003 IEEE Workshop on, page 113--119. (2003)T. Blaschke. Advances in Techniques for Analysis of Remotely Sensed Data, 2003 IEEE Workshop on, page 113--119. (2003)3 years and 9 months agoby mozaher1
- analysis,
- based
- classification,
- contextual
- database,
- databases,
- extraction,
- field
- geo
- geographic
- ground
- high
- human
- image
- imagery,
- information
- instantaneous
- interpretation
- machine
- man
- methods,
- object
- of
- pixel
- processing,
- remote
- resolution
- segmentation,
- sensing
- sensing,
- sensors,
- skills,
- spatial
- system
- systems,
- theory,
- view,
- visual
analysis, based classification, contextual database, databases, extraction, field geo geographic ground high human image imagery, information instantaneous interpretation machine man methods, object of pixel processing, remote resolution segmentation, sensing sensing, sensors, skills, spatial system systems, theory, view, visual - T. Athanasiadis, P. Mylonas, Y. Avrithis, and S. Kollias. Circuits and Systems for Video Technology, IEEE Transactions on 17(3):298-312 (March 2007)T. Athanasiadis, P. Mylonas, Y. Avrithis, and S. Kollias. Circuits and Systems for Video Technology, IEEE Transactions on 17(3):298-312 (March 2007)3 years and 9 months agoby mozaher3
- (mathematics)automatic
- algorithms,
- analysis,
- annotation,
- blending
- context
- extraction,
- feature
- features,
- fuzzy
- global
- growing
- image
- knowledge
- knowledge,
- knowledge-assisted
- labeling,
- multimedia
- object
- ontological
- recursive
- region
- representation,
- segmentation,
- semantic
- set
- sets,
- shortest
- spanning
- taxonomic
- theory,
- tree
- tree,
- trees
- visual
- watershed
(mathematics)automatic algorithms, analysis, annotation, blending context extraction, feature features, fuzzy global growing image knowledge knowledge, knowledge-assisted labeling, multimedia object ontological recursive region representation, segmentation, semantic set sets, shortest spanning taxonomic theory, tree tree, trees visual watershed - P.L. Correia, and F. Pereira. Image Processing, IEEE Transactions on 12(2):186-200 (February 2003)P.L. Correia, and F. Pereira. Image Processing, IEEE Transactions on 12(2):186-200 (February 2003)3 years and 9 months agoby mozaher2
- S. Chabrier, B. Emile, C. Rosenberger, and H. Laurent. Eurasip Journal on Applied Signal Processing 15(3):298-312 (March 2006)S. Chabrier, B. Emile, C. Rosenberger, and H. Laurent. Eurasip Journal on Applied Signal Processing 15(3):298-312 (March 2006)3 years and 9 months agoby mozaher2
- (mathematics)automatic
- algorithms,
- analysis,
- annotation,
- blending
- context
- extraction,
- feature
- features,
- fuzzy
- global
- growing
- image
- knowledge
- knowledge,
- knowledge-assisted
- labeling,
- multimedia
- object
- ontological
- recursive
- region
- representation,
- segmentation,
- semantic
- set
- sets,
- shortest
- spanning
- taxonomic
- theory,
- tree
- tree,
- trees
- visual
- watershed
(mathematics)automatic algorithms, analysis, annotation, blending context extraction, feature features, fuzzy global growing image knowledge knowledge, knowledge-assisted labeling, multimedia object ontological recursive region representation, segmentation, semantic set sets, shortest spanning taxonomic theory, tree tree, trees visual watershed