Guillaume Delyon

Bhattacharyya distance as a contrast parameter for statistical processing of noisy optical images

In many imaging applications, the measured optical images are perturbed by strong fluctuations or boise. This can be the case, for example, for coherent-active or low-flux imagery. In such cases, the noise is not Gaussian additive and the definition of a contrast parameter between two regions in the image is not always a straightforward task. We show that for noncorrelated noise, the Bhattacharyya distance can be an efficient candidate for contrast definition when one uses statistical algorithms for detection, location, or segmentation. We demonstrate with numerical simulations that different images with the same Bhattacharyya distance lead to equivalent values of the performance criterion for a large number of probability laws. The Bhattacharyya distance can thus be used to compare different noisy situations and to simplify the analysis and the specification of optical imaging systems.

SAR image segmentation by stochastic complexity minimization with a nonparametric noise model

We analyze the generalization of a parametric segmentation technique adapted to Gamma-distributed synthetic aperture radar (SAR) images to nonparametric noise models. This approach is based on a polygonal grid which can have an arbitrary topology and whose number of regions and regularity of its boundaries are obtained by minimizing the stochastic complexity of a quantified version on Q levels of the image. It thus leads to a criterion without parameters to be tuned by the user and adapted to different noise models. We analyze the influence of the quantization scheme and of the optimization procedure on the quality of the partitioning. We then compare the performance of the proposed approach to the parametric one on synthetic images. Finally, we show results obtained on real images and compared with a standard segmentation algorithm of SAR images

Mixed segmentation-detection-based technique for point target detection in nonhomogeneous sky

This paper deals with point target detection in infrared images of the sky for which there are local variations of the gray level mean value. We show that considering a simple image model with the gray level mean value varying as a linear or a quadratic function of the pixel coordinates can improve mixed segmentation-detection performance in comparison to homogeneous model-based approaches.

Minimal Stochastic Complexity Image Partitioning With Unknown Noise Model

We present a generalization of a new statistical technique of image partitioning into homogeneous regions to cases where the family of the probability laws of the gray-level fluctuations is a priori unknown. For that purpose, the probability laws are described with step functions whose parameters are estimated. This approach is based on a polygonal grid which can have an arbitrary topology and whose number of regions and regularity of its boundaries are obtained by minimizing the stochastic complexity of the image. We demonstrate that efficient homogeneous image partitioning can be obtained when no parametric model of the probability laws of the gray levels is used and that this approach leads to a criterion without parameter to be tuned by the user. The efficiency of this technique is compared to a statistical parametric technique on a synthetic image and is compared to a standard unsupervised segmentation method on real optical images

Photon-noise effect on detection in coherent active images

We analyze photon-noise effects on target detection performance in low-flux coherent active imagery systems. We show that when photon noise is expected, the performance of classical detection techniques designed for pure and fully developed speckle images can be improved with no increase in algorithm complexity. Furthermore, the mean photon number under which photon noise becomes sensitive is higher when the target and background mean values are unknown than in the idealized case, where they are assumed to be known, and when the reflectivity ratio between the target and the background is low.

Stochastic Complexity based Image Segmentation with unknown Noise Model

We propose a general statistical image segmentation method which does not need any a priori knowledge of the probability density functions (PDF) of the grey levels of the image. This method is based on the minimization of the stochastic complexity (Minimum Description Length principle) which leads to optimize a criterion without parameter to be tuned by the user which is adapted to the PDF of the grey levels of the image. We apply this method to three partition descriptors: a polygonal active contour, a level set implementation and a polygonal active grid. We illustrate the technique on real images.

Non-parametric partitioning of SAR images

We describe and analyse a generalization of a parametric segmentation technique adapted to Gamma distributed SAR images to a simple non parametric noise model. The partition is obtained by minimizing the stochastic complexity of a quantized version on Q levels of the SAR image and lead to a criterion without parameters to be tuned by the user. We analyse the reliability of the proposed approach on synthetic images. The quality of the obtained partition will be studied for different possible strategies. In particular, one will discuss the reliability of the proposed optimization procedure. Finally, we will precisely study the performance of the proposed approach in comparison with the statistical parametric technique adapted to Gamma noise. These studies will be led by analyzing the number of misclassified pixels, the standard Hausdorff distance and the number of estimated regions.