Titlebook: Statistical Image Processing Techniques for Noisy Images; An Application-Orien Fran?ois Goudail,Philippe Réfrégier Book 2004 Springer Scien

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Applications of Statistical Correlation Techniques to Different Physical Noises, and more new imagery systems based on various physical phenomena are developed. Indeed, each of these systems may have different noise characteristics and statistical decision and estimation theory provides an efficient way of designing adapted processing algorithms.

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Introduction,te sensing for earth monitoring, robotic applications, telecommunications, scientific instrumentation for astronomy or biological research, military optoelectronic, and radar surveillance systems. The tremendous development of these fields of application requires the design of appropriate digital im

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Linear Filters: Heuristic Theory and Stability, recognition). They are based on correlations between the searched objects (or a linear combination of them) and the analyzed scene. The correlation operation has several advantages in the context of image processing. First, it is an intrinsically position invariant recognition method which makes it

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Statistical Correlation Techniques,es. It makes it possible to introduce very naturally additional constraints, such as regularization terms, and to handle large training sets in an efficient way. However, there is no proof that these algorithms optimize the parameters which are of interest for the user. These parameters may be the p

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Applications of Statistical Correlation Techniques to Different Physical Noises,o the noise that actually affects the images. The efficiency of this approach stems from the fact that it is based on a physical model of the image and of the noise:The derived algorithms are thus, by construction, adapted to these perturbations. This approach has many applications nowadays, as more

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Some Developments of the Polygonal Statistical Snake and Their Applications,t prior knowledge of the statistical parameters of the target and of the background. We present in this chapter further improvements and generalizations of this technique, while keeping reasonable computation times.

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An Example of Application: Processing of Coherent Polarimetric Images,active polarimetric imagery. This will provide us with the opportunity of introducing important new aspects of the design of algorithms based on decision and estimation theory. In particular, we will analyze how to design computationally efficient detection and segmentation algorithms when the noise

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Introduction,ard in order to explain this situation. Two of them are of prime importance for the subject of this book: the progress, the diversity, and the diffusion of different imaging sensors and the increase of the computational power of digital processors.