DocumentCode :
2771333
Title :
Sequential causal estimation and learning from time-varying images
Author :
Chalasani, Rakesh ; Cinar, Goktug T. ; Principe, Jose C.
Author_Institution :
Dept. of Electr. & Comput. Eng., Univ. of Florida, Gainesville, FL, USA
fYear :
2012
fDate :
10-15 June 2012
Firstpage :
1
Lastpage :
5
Abstract :
Dynamic models are used in modeling the perceptual systems with hierarchies. But most of the models assume Gaussian statistics on the underlying causes. In this paper we try to develop a basic building block for these hierarchical models where the causes are assumed to be non-Gaussian. We describe a sequential dual estimation framework for inferring the hidden states and unknown causes/inputs while learning the parameters of the model. It is observed that the algorithm is able to extract bases from the time varying image sequence that resembles receptive fields of the simple cells in V1. In addition, the dynamical model gives us the ability to deconvolve spatial and temporal changes in the image sequence.
Keywords :
Gaussian processes; estimation theory; image sequences; learning (artificial intelligence); Gaussian statistics; perceptual systems; sequential causal estimation; sequential causal learning; sequential dual estimation; time varying image sequence; Computational modeling; Cost function; Estimation; Heuristic algorithms; Image reconstruction; Image sequences; Visualization;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks (IJCNN), The 2012 International Joint Conference on
Conference_Location :
Brisbane, QLD
ISSN :
2161-4393
Print_ISBN :
978-1-4673-1488-6
Electronic_ISBN :
2161-4393
Type :
conf
DOI :
10.1109/IJCNN.2012.6252480
Filename :
6252480
Link To Document :
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