<title>Fast algorithm for region snake-based segmentation adapted to physical noise models and application to object tracking</title>

Abstract

Algorithms for object segmentation are crucial in many image processing applications. During past years, active contour models 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 map 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 and can thus be adapted to different kinds of random fields which can describe the input image. In this paper out 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.