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Noise is ubiquitous in real life and changes image acquisition and processing characteristics in an uncontrolled manner. Highly sophisticated image processing algorithms developed for clean images often malfunction when they are used for noisy images. For example, hidden Markov Gauss mixture models (HMGMM) have been shown to perform well in image segmentation applications, but they have also proved...
Image segmentation is an important tool in image processing and can serve as an efficient front end to sophisticated algorithms and thereby simplify subsequent processing. We develop a multiclass image segmentation method using hidden Markov Gauss mixture models (HMGMMs) and provide examples of segmentation of aerial images and textures. HMGMMs incorporate supervised learning, fitting the observation...
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