Comparing the Performance of Connectionist and Statistical Classifiers on an Image Segmentation Problem

In the development of an image segmentation system for real time image processing applications, we apply the classical decision anal(cid:173) ysis paradigm by viewing image segmentation as a pixel classifica.(cid:173) We use supervised training to derive a classifier for our system from a set of examples of a particular pixel classification problem. Classifiers are derived using all three methods, and the performance of all of the classi(cid:173) fiers on the training data set as well as on 3 separate entire test images is measured.