Anomalies detection on spatially inhomogeneous polyzonal images

The text deals with the problem of detecting anomalies on a background of multi-dimensional images. We synthesized a detection algorithm based on the use of doubly stochastic models of random fields and which requires pre-filtering the image. We propose to use the modified Kalman filter. We also investigated an efficiency of extended signals detection on real images. It is shown that the resulting algorithm has a higher efficiency than the known algorithms which based on the traditional autoregressive image description. The gain is explained by more adequate description of the real inhomogeneous material using doubly stochastic models.