Missile tracking using knowledge-based adaptive thresholding

In this paper, we apply a knowledge-based segmentation method developed for still and video images to the problem of tracking missiles and high speed projectiles. Since we are only interested in segmenting a portion of the missile (namely, the nose cone), we use our segmentation procedure as a method of adapting thresholding. The key idea is to utilize a priori knowledge about the objects present in the image, e.g. missile and background, introduced via Bayes' rule. Posterior probabilities obtained in this way are anisotropically smoothed, and the image segmentation is obtained via MAP classifications of the smoothed data. When segmenting sequences of images, the smoothed posterior probabilities of past frames are used as prior distributions in succeeding frames.