Image segmentation using maximum flow

Eva Križman (2017) Image segmentation using maximum flow. EngD thesis.

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Abstract

In this thesis we consider image segmentation using maximum flow. In the first part of the thesis we present in detail the maximum flow problem and its dual problem, the minimum cut problem. We describe two algorithms for solving these two problems, the Ford-Fulkerson algorithm and Dinic algorithm. In the second part of the thesis we introduce the concept of image segmentation and we list some methods that are used for image segmentation. We describe some of them: the thresholding method, clustering methods, the region-growing methods and segmentation on graphs. In the last chapter we present in more detail segmentation on graphs using maximum flow. We represent a given image with a weighted graph. Then we find maximum flow and minimum cut in this graph. Using minimum cut we can separate pixels to those that belong to the foreground and those that belong to the background. Pseudo code is given for all the algorithms used and their time complexity is analyzed. At the end of the thesis we discuss some problems that arise in this kind of segmentation.

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