Two-dimensional image boundary estimation by use of likelihood maximization and Kalman filtering

This paper formulates the problem of object boundary estimation in noisy black and white images as a state sequence estimation problem for a discrete-time Markov process. Based upon this formulation and the resulting likelihood function a suboptimal estimation algorithm is developed for elliptically shaped boundaries. Appropriate transition probabilities are calculated by developing a dynamic model for boundary generation and implementing a Kalman filter.