Image models and the definition of image entropy applied to the problem of unsupervised segmentation

Abstract

Region segmentation of digital imges by unsupervised thresholding is a common, conceptually simple and important branch of image processing and analysis. Its applications range from that of simple binarization to serving as a useful pre-processing stage for operations such as pattern recognition and image restoration. While many different algorithms have been proposed for the automatic selection of the "correct" threshold the results vary widely in their general usefulness. A class of selection schemes is based on the principle of maximum entropy. This formalism, While effective, is usually involed without reference to its origins or its relationship to images. This thesis attempts to clarify the definition of what is meant by the entropy of an image, to which end various image and Image segmentation models are discussed and proposed. Some apparent shortcomings related to the use of the Shannon entropy formula are addressed and the outcome of the research is applied to the problem of threshold selection. The results indicate a marked improvement in performance of methods using some form(s) of context-related information over those which simply apply the entropy formula without regard to its spatially insensitive nature. Evaluation of results and processes is usually based