His primary areas of investigation include Artificial intelligence, Image processing, Computer vision, Pattern recognition and Expectation–maximization algorithm. His Relevance research extends to Artificial intelligence, which is thematically connected. Nikolas P. Galatsanos studies Image restoration which is a part of Image processing.
His study in the fields of Iterative reconstruction, Discrete cosine transform and Picture processing under the domain of Computer vision overlaps with other disciplines such as Diagonalizable matrix. Nikolas P. Galatsanos has researched Pattern recognition in several fields, including Word error rate, Object detection, Constrained optimization and Receiver operating characteristic. His Projections onto convex sets course of study focuses on Algorithm and Mathematical optimization.
Nikolas P. Galatsanos spends much of his time researching Artificial intelligence, Computer vision, Algorithm, Image restoration and Pattern recognition. His Artificial intelligence study frequently draws parallels with other fields, such as Expectation–maximization algorithm. His research in Algorithm intersects with topics in Monte Carlo method and Mathematical optimization.
His work carried out in the field of Image restoration brings together such families of science as Estimation theory, Filter, Template matching and Iterative method. His biological study spans a wide range of topics, including Object detection, Bayesian probability and Cluster analysis. His work on Digital image as part of general Image processing research is frequently linked to Maxima and minima, thereby connecting diverse disciplines of science.
Nikolas P. Galatsanos mainly investigates Artificial intelligence, Pattern recognition, Bayesian inference, Computer vision and Bayesian probability. His studies deal with areas such as Machine learning and Maximum a posteriori estimation as well as Artificial intelligence. In most of his Pattern recognition studies, his work intersects topics such as Expectation–maximization algorithm.
His Image recovery, Normalization and Discrete cosine transform study in the realm of Computer vision interacts with subjects such as Recovery method. His Bayesian probability research includes themes of Deconvolution, Blind deconvolution, Mathematical economics and Mathematical optimization. His research in the fields of Image restoration overlaps with other disciplines such as Information protection policy.
Nikolas P. Galatsanos mainly focuses on Artificial intelligence, Image processing, Expectation–maximization algorithm, Pattern recognition and Maximum a posteriori estimation. Nikolas P. Galatsanos has included themes like Relevance and Computer vision in his Artificial intelligence study. His work on Watermark, Normalization and Discrete cosine transform as part of general Computer vision study is frequently connected to Information protection policy, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
His research is interdisciplinary, bridging the disciplines of Digital watermarking and Image processing. His work deals with themes such as Deconvolution, Blind deconvolution, Graphical model and Variable-order Bayesian network, which intersect with Expectation–maximization algorithm. His Maximum a posteriori estimation study also includes fields such as
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
The variational approximation for Bayesian inference
D.G. Tzikas;A.C. Likas;N.P. Galatsanos.
IEEE Signal Processing Magazine (2008)
A support vector machine approach for detection of microcalcifications
I. El-Naqa;Yongyi Yang;M.N. Wernick;N.P. Galatsanos.
IEEE Transactions on Medical Imaging (2002)
Regularized reconstruction to reduce blocking artifacts of block discrete cosine transform compressed images
Yongyi Yang;N.P. Galatsanos;A.K. Katsaggelos.
IEEE Transactions on Circuits and Systems for Video Technology (1993)
Methods for choosing the regularization parameter and estimating the noise variance in image restoration and their relation
N.P. Galatsanos;A.K. Katsaggelos.
IEEE Transactions on Image Processing (1992)
Projection-based spatially adaptive reconstruction of block-transform compressed images
Yongyi Yang;N.P. Galatsanos;A.K. Katsaggelos.
IEEE Transactions on Image Processing (1995)
Digital watermarking robust to geometric distortions
Ping Dong;J.G. Brankov;N.P. Galatsanos;Yongyi Yang.
IEEE Transactions on Image Processing (2005)
A similarity learning approach to content-based image retrieval: application to digital mammography
I. El-Naqa;Yongyi Yang;N.P. Galatsanos;R.M. Nishikawa.
IEEE Transactions on Medical Imaging (2004)
Digital restoration of multichannel images
N.P. Galatsanos;R.T. Chin.
IEEE Transactions on Acoustics, Speech, and Signal Processing (1989)
A spatially constrained mixture model for image segmentation
K. Blekas;A. Likas;N.P. Galatsanos;I.E. Lagaris.
IEEE Transactions on Neural Networks (2005)
Miles N Wernick;Oliver Wirjadi;Oliver Wirjadi;Dean Chapman;Zhong Zhong.
Physics in Medicine and Biology (2003)
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: