Olivier Germain

Edge location in SAR images: performance of the likelihood ratio filter and accuracy improvement with an active contour approach

The likelihood ratio edge detector is an efficient filter for the segmentation of synthetic aperture radar (SAR) images. We show that this filter provides biased location of the edge, when the window does not have the same orientation as the edge. A phenomenological model is proposed to characterize this bias. We then introduce an efficient technique to refine edge location: the statistical active contour. The combination of these two methods permits to achieve accurate and regularized edge location.

Optimal snake-based segmentation of a random luminance target on a spatially disjoint background.

We describe a segmentation processor that is optimal for tracking the shape of a target with random white Gaussian intensity appearing on a random white Gaussian spatially disjoint background. This algorithm, based on an active contours model (snakes), consists of correlations of binary references with preprocessed versions of the scene image. This result can provide a practical method to adapt the reference image to correlation techniques.

Synthetic aperture Radar oil spill segmentation by stochastic complexity minimization

We present a new algorithm for oil spill segmentation in synthetic aperture radar (SAR) images, using the minimum description length (MDL) principle and a polygonal active grid. This algorithm is based on two steps: a first partitioning step into homogeneous regions and a second classification step with an automatic MDL thresholding. The obtained method allows one to segment the different candidate oil spills in an image automatically and in a few seconds.

On the bias of the likelihood ratio edge detector for SAR images

The likelihood ratio edge detector is an efficient filter for the segmentation of SAR images. The authors show that this filter provides biased location of the edge if it is not an ideal step edge of known orientation. A simple model enables the authors to interpret this observation and to evaluate the bias.

Optimal snake segmentation of target and background with independent Gamma density probabilities, application to speckled and preprocessed images

Abstract We propose in this paper a snake-based segmentation processor to track the shape of a target with random white intensity appearing on a random white spatially disjoint background. We study the optimal solution for Gamma laws and we discuss the relevance of such statistics for realistic situations. This algorithm, based on an active contour model (snakes), consists in correlations of a binary reference with the scene image or with pre-processed version of the scene image. This method is a generalization of correlation techniques and thus opens new applications for digital and optical correlators.

Statistical active grid for segmentation refinement

Abstract We present a new statistical method, based on a deformable partition called “active grid” for the semi-supervised segmentation of an image composed of several homogeneous regions. This approach allows one to efficiently refine a rough pre-segmentation with a computing time below half a second for 256×256 images (on a standard 700 MHz PC).

Snake-based method for the segmentation of objects in multichannel images degraded by speckle

We recently proposed a new approach for the segmentation of speckled images based on active contours (snakes) [e.g., Opt. Commun. 137, 382 (1997)]. We propose an extension of this approach to multichannel data. Two solutions are compared based on hypotheses on the possible mean intensity variation between the channels. Each solution is optimal for a certain class of input images, but one solution shows better or equivalent performance for both input image classes. This result opens new perspectives for the segmentation of multichannel images with the snake-based approach.

Edge detection and localization in SAR images: a comparative study of global filtering and active contour approaches

We address the problem of edge detection in synthetic aperture radar (SAR) images and we particularly focus on the precision with which the frontier of an object can be determined. Recently, an edge detection global filter has been proposed. It involves a two-region analyzing window whose properties greatly influence its efficiency. In this paper, we will study the filter detection and localization abilities as functions of the analyzing window geometry, namely its shape, size and orientation. We will then introduce another approach to segment an object in a SAR image: the statistical active contour. The spatial precision of these two approaches will be finally compared and discussed.

Statistical multiregion snake-based segmentation

We have recently proposed a new statistical approach based on active contours (snakes) for the segmentation of a unique object in an image. In this article, we address the case of an image composed of several different regions. We propose an extension of snake to a deformable partition of the image with a fixed number of regions. This active grid allows a semi-supervised segmentation of the image.