Result 1 to 20 of 22 total
A comparison of dictionary based approaches to inpainting and denoising with an emphasis to independent component analysis learned dictionaries. (English)
Inverse Probl. Imaging 5, No. 4, 815-841 (2011).
1
Nonlinear band expansion and 3D nonnegative tensor factorization for blind decomposition of magnetic resonance image of the brain. (English)
Vigneron, Vincent (ed.) et al., Latent variable analysis and signal separation. 9th international conference, LVA/ICA 2010, St. Malo, France, September 27‒30, 2010. Proceedings. Berlin: Springer (ISBN 978-3-642-15994-7/pbk). Lecture Notes in Computer Science 6365, 490-497 (2010).
2
Nonlinear band expansion and 3D nonnegative tensor factorization for blind decomposition of magnetic resonance image of the brain (English)
LVA/ICA, 490-497 (2010).
3
Dependent component analysis for blind restoration of images degraded by turbulent atmosphere. (English)
Neurocomputing 72, No. 10-12, 2682-2692 (2009).
4
Second and fourth order statistics-based reduced polynomial rooting direction finding algorithms. (English)
Signal Process. 89, No. 6, 1050-1060 (2009).
5
Unsupervised decomposition of low-intensity low-dimensional multi-spectral fluorescent images for tumour demarcation (English)
Medical Image Analysis 13, No. 3, 507-518 (2009).
6
Wavelet packets approach to blind separation of statistically dependent sources. (English)
Neurocomputing 71, No. 7-9, 1642-1655 (2008).
7
Blind signal deconvolution as an instantaneous blind separation of statistically dependent sources. (English)
Davies, Mike E. (ed.) et al., Independent component analysis and signal separation. 7th international conference, ICA 2007, London, UK, September 9‒12, 2007. Proceedings. Berlin: Springer (ISBN 978-3-540-74493-1/pbk). Lecture Notes in Computer Science 4666, 504-511 (2007).
8
Blind signal deconvolution as an instantaneous blind separation of statistically dependent sources (English)
ICA, 504-511 (2007).
9
Robust blind separation of statistically dependent sources using dual tree wavelets (English)
ICIP (1), 433-436 (2007).
10
Non-negative matrix factorization approach to blind image deconvolution. (English)
Rosca, Justinian (ed.) et al., Independent component analysis and blind signal separation. 6th international conference, ICA 2006, Charleston, SC, USA, March 5‒8, 2006. Proceedings. Berlin: Springer (ISBN 3-540-32630-8/pbk). Lecture Notes in Computer Science 3889, 966-973 (2006).
11
Kernel based algorithms for mining huge data sets. Supervised, semi-supervised, and unsupervised learning. (English)
Studies in Computational Intelligence 17. Berlin: Springer (ISBN 3-540-31681-7/hbk). xvi, 260~p. EUR~96.25; SFR~152.50 (2006).
12
Kernel based algorithms for mining huge data sets: supervised, semi-supervised, and unsupervised learning (English)
Studies in Computational Intelligence 17 (2006).
13
Non-negative matrix factorization approach to blind image deconvolution (English)
ICA, 966-973 (2006).
14
Unsupervised learning with stochastic gradient. (English)
Neurocomputing 68, 130-160 (2005).
15
Independent component analysis for classifying multispectral images with dimensionality limitation (English)
I. J. Information Acquisition 1, No. 3, 201-216 (2004).
16
Space-time variant blind source separation with additive noise (English)
ICA, 240-247 (2004).
17
Sparse coding blind source separation through powerline. (English)
Neurocomputing 48, No.1-4, 1015-1020 (2002).
18
Design and implementation of a hardware-in-the-loop simulator for a semi-automatic guided missile system. (English)
Simul. Pr. Theory 7, No. 2, 107-123 (1999).
19
Design and implementation of a hardware-in-the-loop simulator for a semi-automatic guided missile system (English)
Simul. Pr. Theory 7, No. 2, 107-123 (1999).
20
Result 1 to 20 of 22 total
Start
Contact
Print
General Help
