The paper (PDF, 10 pages, 360K) describes the algorithm behind Pixcavator. The algorithm is presented in detail in the wiki but this is a new and improved exposition. I reconsidered some of the terminology, re-wrote the pseudocode, and improved illustrations. There is also a gap in the wiki - when an edge is added to the image, case 4 is missing. I’ll have to re-write a few articles. The presentation in the paper is less detailed (in terms of examples, images etc) but it is a bit more thorough.

Abstract: The paper provides a method of image segmentation of binary and gray scale images. For binary images, the method captures not only connected components but also the holes. For gray scale images, there are two kinds of “connected components” – dark regions surrounded by lighter areas or light regions surrounded by darker areas.

The long term goal is to design a computer vision system “from first principles”. The last sentence in the abstract is one such principle. Keep in mind (of course) that if every dark region surrounded by a lighter area is an object, it does not mean that every object is a dark region surrounded by a lighter area (or vice versa). In a way, these are “potential” objects and you still have to filter and/or group them to find the “real” ones. So there must be more first principles.

The paper does not go far beyond this stage. The main step is – all potential objects are recorded in the “topology graph” (“frame graph” in the wiki). Then only one method of filtering is presented (the one based on size).

All feedback is welcome.