Two Experiments on Statistical Image Segmentation.

Recently there has been a considerable research interest in applying statistical pattern recognition theory to image segmentation. As the image is rich in statistical information, effective segmentation of images into meaningful parts can be performed by using statistical techniques. In this report, we present segmentation results on infrared and reconnaissance images using two different statistical pattern recognition methods. The first experiment is on the Alabama data base infrared images using the Fishers linear discriminant analysis 1. To preserve the inter-pixel dependence as much as possible, measurements are taken in the form of a 3 x 3 matrix. That is we are dealing with matrix measurements instead of vector measurements as typically considered in statistical pattern recognition. Author