Stella X. Yu : Papers / Google Scholar

Finding Dots: Segmentation as Popping out Regions from Boundaries
Elena Bernardis and Stella X. Yu
IEEE Conference on Computer Vision and Pattern Recognition, San Francisco, California, 13-18 June 2010
Paper | Poster

Many applications need to segment out all small round regions in an image. This task of finding dots can be viewed as a region segmentation problem where the dots form one region and the areas between dots form the other. We formulate it as a graph cuts problem with two types of grouping cues: short-range attraction based on feature similarity and long-range repulsion based on feature dissimilarity. The feature we use is a pixel-centric relational representation that encodes local convexity: Pixels inside the dots and outside the dots become sinks and sources of the feature vector. Normalized cuts on both attraction and repulsion pop out all the dots in a single binary segmentation. Our experiments show that our method is more accurate and robust than state-of-art segmentation algorithms on four categories of microscopic images. It can also detect textons in natural scene images with the same set of parameters.

image segmentation, spectral graph partitioning, watershed, attraction and repulsion