Finding Dots: Segmentation as Popping out Regions from Boundaries

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 three categories of microscopic images. It can also detect textons in real scene images with the same set of parameters.

• Type: Conference
• Year: 2010
• Where: CVPR
• Authors: Elena Bernardis, Stella Yu
• Keywords: Computer Vision, CVPR 2010, Dots, Region Segmentation Problem, Single Binary Segmentation