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

Part of Advances in Neural Information Processing Systems 2 (NIPS 1989)

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Sheri Gish, W. Blanz


In this study, we test the suitability of a connection(cid:173)

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) tion task. We use supervised training to derive a classifier for our system from a set of examples of a particular pixel classification problem. ist method against two statistical methods, Gaussian maximum likelihood classifier and first, second, and third degree polynomial classifiers, for the solution of a "real world" image segmentation problem taken from combustion research. 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.