No abstract

We propose a technique to increase the robustness of a snake-based segmentation method originally introduced to track the shape of a target with random white Gaussian intensity upon a random white Gaussian background. Because these statistical conditions are not always fulfilled with optronic images, we describe two improvements that increase the field of application of this approach. We first show that regularized whitening preprocessing allows one to apply the original method successfully for a target with a correlated texture upon a correlated background. We then introduce a simple multiscale approach that increases the robustness of the segmentation against the initialization of the snake (i.e., the initial shape used for the segmentation). These results provide a robust and practical method for determination of the reference image for correlation techniques.

Recently new approaches for location and /or segmentation of objects with unknown gray levels embedded in non-overlapping noise have been proposed. These techniques are based on the Statistically Independent Region (SIR) model and are optimal in the maximum likelihood sense. In this paper, we review their theoretical bases and propose a unified approach which enlarges their field of application.

A new snake based technique for target segmentation is presented. This approach can be implemented with optical correlators and thus enlarge their field of application.

Algorithms for object segmentation are crucial in many image processing applications. During past years, active contour models (snakes) have been widely used for finding the contours of objects. This segmentation strategy is classically edge based in the sense that the snake is driven to fit the maximum of an edge iiiap of the scene. We have recently proposed a region-based snake approach, that can be implemented using a fast algorithm, to segment an object in an image. The algorithms, optimal in the Maximum Likelihood sense, are based on the calculus of the statistics of the inner and the outer regions (defined by the snake) and can thus be adapted to different kinds of random fields which can describe the input image. In this paper our aim is to study this approach for tracking application in optronic images. We first show the relevance of using a priori information on the statistical laws of the input image in the case of Gaussian statistics which are well adapted to describe optronic images when a whitening preprocessing is used. We will then characterize the performance of the fast algorithm implementation of the used approach and we will apply it to tracking applications. The efficiency of the proposed method will be shown on real image sequences.