Segmentation and boundary detection using multiscale intensity measurements

Abstract

Image segmentation is difficult because objects may dif- fer from their background by any of a variety of properties that can be observed in some, but often not all scales. A further complication is that coarse measurements, applied to the image for detecting these properties, often average over properties of neighboring segments, making it diffi- cult to separate the segments and to reliably detect their boundaries. Below we present a method for segmentation that generates and combines multiscale measurements of in- tensity contrast, texture differences, and boundary integrity. The method is based on our former algorithm SWA, which efficiently detects segments that optimize a normalized-cut- like measure by recursively coarsening a graph reflecting similarities between intensities of neighboring pixels. In this process aggregates of pixels of increasing size are grad- ually collected to form segments. We intervene in this pro- cess by computing properties of the aggregates and modi- fying the graph to reflect these coarse scale measurements. This allows us to detect regions that differ by fine as well as coarse properties, and to accurately locate their bound- aries. Furthermore, by combining intensity differences with measures of boundary integrity across neighboring aggre- gates we can detect regions separated by weak, yet consis- tent edges.