Modeling and Segmentation of Noisy and Textured Images Using Gibbs Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Vector quantization and signal compression
Vector quantization and signal compression
Graphical models for machine learning and digital communication
Graphical models for machine learning and digital communication
Distributed Source Coding: Symmetric Rates and Applications to Sensor Networks
DCC '00 Proceedings of the Conference on Data Compression
Signal Processing - Special section: Distributed source coding
Low-complexity coding and source-optimized clustering for large-scale sensor networks
ACM Transactions on Sensor Networks (TOSN)
Code design for quadratic Gaussian multiterminal source coding: the symmetric case
ISIT'09 Proceedings of the 2009 IEEE international conference on Symposium on Information Theory - Volume 3
Distributed source coding without Slepian-Wolf compression
ISIT'09 Proceedings of the 2009 IEEE international conference on Symposium on Information Theory - Volume 2
Design of scalable decoders for sensor networks via Bayesian network learning
IEEE Transactions on Communications
Distributed Estimation and Detection for Sensor Networks Using Hidden Markov Random Field Models
IEEE Transactions on Signal Processing
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
Rate-distortion performance of DPCM schemes for autoregressive sources
IEEE Transactions on Information Theory
Encoding of correlated observations
IEEE Transactions on Information Theory
Distributed source coding using syndromes (DISCUS): design and construction
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Generalized coset codes for distributed binning
IEEE Transactions on Information Theory
On Multiterminal Source Code Design
IEEE Transactions on Information Theory
Distributed estimation and quantization
IEEE Transactions on Information Theory
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An approach to scalable joint source decoding in large-scale sensor networks, based on Markov-random filed (MRF) modeling of the spatio-temporal correlation in the observations is presented. This approach exploits the correlation among a multitude of sensors for joint decoding at a central decoder, while using simple distributed quantizers in individual sensors. The decoder derivations are provided for Slepian-Wolf coded quantization based on both sample-by-sample (scalar) binning and vector binning schemes constructed via channel code partitioning. Simulation results are presented to demonstrate the performance achievable with the proposed decoding approach.