Paper
29 January 2007 Maximum-entropy expectation-maximization algorithm for image processing and sensor networks
Author Affiliations +
Proceedings Volume 6508, Visual Communications and Image Processing 2007; 65080D (2007) https://doi.org/10.1117/12.698056
Event: Electronic Imaging 2007, 2007, San Jose, CA, United States
Abstract
In this paper, we propose a maximum-entropy expectation-maximization algorithm. We use the proposed algorithm for density estimation. The maximum-entropy constraint is imposed in order to ensure smoothness of the estimated density function. The exact derivation of the maximum-entropy expectation-maximization algorithm requires determination of the covariance matrix combined with the maximum entropy likelihood function, which is difficult to solve directly. We therefore introduce a new lower-bound for the EM algorithm derived by using the Cauchy-Schwartz inequality to obtain a suboptimal solution. We use the proposed algorithm for function interpolation and image segmentation. We propose the use of the EM algorithm for image recovery from randomly sampled data and signal reconstruction from randomly scattered sensors. We further propose to use our approach to maximum-entropy expectation-maximization (MEEM) in all of these applications. Computer simulation experiments are used to demonstrate the performance of our algorithm in comparison to existing methods.
© (2007) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Hunsop Hong and Dan Schonfeld "Maximum-entropy expectation-maximization algorithm for image processing and sensor networks", Proc. SPIE 6508, Visual Communications and Image Processing 2007, 65080D (29 January 2007); https://doi.org/10.1117/12.698056
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Cited by 3 scholarly publications.
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KEYWORDS
Expectation maximization algorithms

Reconstruction algorithms

Image processing algorithms and systems

Image segmentation

Image processing

Image restoration

Sensor networks

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