Semiautomatic Segmentation with Compact Shapre Prior

We present a semiautomatic segmentation algorithm, that can segment an object of interest from its background based on a single user selected seed. We are able to obtain reliable and robust segmentation with such low user interaction by assuming that the object to be segmented is of compact shape (we define this assumption later). We base our work on the powerful Graph Cut segmentation algorithm of Boykov and Jolly [2]. As additional benefit of incorporating the compact shape prior we are able to bias the graph cuts segmentation framework towards larger objects. It helps to counteract the well known bias of [2] to shorter segmentation boundaries. Segmentation results are quite sensitive to the choice of parameters, and so another contribution of our paper is that we show how to select the parameters automatically. We demonstrate the effectiveness of our method on the challenging industrial application of transistor gate segmentation in an integrated chip, for which it produces highly accurate results in realtime.