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

Abstract

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.