Two-Dimensional Hidden Markov Model for Classification of Continuous-Valued Noisy Vector Fields

Abstract

In this paper we present a statistical model with a nonsymmetric half-plane (NSHP) region of support for two-dimensional continuous-valued vector fields. It has the simplicity, efficiency, and ease of use of the well-known hidden Markov model (HMM) and associated Baum-Welch algorithms for time-series and other one-dimensional problems. At the same time it is able to learn textures on a two-dimensional field. We describe a fast approximate forward procedure for computation of the joint probability density function (pdf) of the vector field as well as an approximate Baum-Welch algorithm for parameter reestimation. Radar and sonar applications include classification of two-dimensional fields such as range versus azimuth or range versus aspect angle data wherein each data point in the field consists of a multi-dimensional feature vector. We test the method using synthetic textures.