Statistical Signal Processing
Uncertainty is present in various forms in numerous information engineering activities, for example, telecommunications, target tracking, sensor data fusion, signal and image processing.
The present interdisciplinary research program at Bristol bridges the Statistics group and the Department of Electrical and Electronic Engineering by promoting the transfer of modern statistical methodology to the area of signal processing using the tools in concrete applications.
Particular interests include approximate inference in large-scale statistical models, applications to communication and coding, machine learning, wavelet methods for data fusion, distributed computations, vesicle tracking in biological image processing and multiscale network visualisation.
Approximate inference in hidden Markov models using iterative active state selection (2006)
Vithanage, CM, Andrieu, C & Piechocki, RJ.
IEEE Signal Processing Letters, vol: 13, Issue: 2, Pages: 65 - 68
A statistical multiscale approach to image segmentation and fusion (2005)
Cardinali, A. and Nason, G.P.
Proceedings of the Eighth International Conference on Information Fusion
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