TITLE: Segmentation of noisy images using information theory based approaches.

AUTHORS: Frederic GALLAND and Philippe REFREGIER (France)

ABSTRACT:

Determining the shape of objects in noisy images is a fundamental task for many applications. We propose here a review of new techniques based on information theory, which allows to determine segmentation techniques without parameter to be tuned by the user and adapted to noisy images. These techniques are quite general since they include polygonal and spline parametric shape descriptions, level set models of contours and polygonal grids to partition images into an arbitrary number of homogeneous regions. This approach can take into account the physical nature of the grey level fluctuations and is thus adapted to different imaging systems (optical, SAR, ...). Furthermore, it can lead to fast algorithms: from a few hundred of milliseconds to a few seconds depending on the complexity of the task to perform on 256 x 256 pixel images.

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