@techreport{TR-IC-07-23,
  number = {IC-07-23},
  author = {F.~P.~G.~Bergo and A.~X.~Falc{\~{a}}o and
            P.~A.~V.~Miranda and L.~M.~Rocha},
  title = {Automatic Image Segmentation by Tree Pruning},
  month = {July},
  year = {2007}, 
  institution = {Institute of Computing, University of Campinas},
  note = {In English, 33 pages.
    \par\selectlanguage{english}\textbf{Abstract}
       The Image Foresting Transform (IFT) is a tool for the design of
       image processing operators based on connectivity, which reduces
       image processing problems into a minimum-cost path forest
       problem in a graph derived from the image. We propose a new
       image operator, which solves segmentation by pruning trees of
       the forest. An IFT is applied to create an optimum path forest
       whose roots are seed pixels, selected inside a desired
       object. In this forest, object and background are connected by
       optimum paths (leaking paths), which cross the object's
       boundary through its "most weakly connected" parts (leaking
       pixels). These leaking pixels are automatically identified and
       their subtrees are eliminated, such that the remaining forest
       defines the object. Tree pruning runs in linear-time, is
       extensive to multidimensional images, is free of ad-hoc
       parameters, requires only internal seeds, and works with
       minimal interference from the heterogeneity of the
       background. These aspects favor solutions that exploit image
       features and object information for automatic segmentation. We
       give a formal definition of the obtained objects and conditions
       to achieve robust segmentation using tree pruning, describe the
       algorithms and evaluate automatic segmentation by tree-pruning
       in two applications: 3D MR-image segmentation of the human
       brain and segmentation of license plates. Given that its most
       competitive approach is the watershed transform by markers, we
       include a comparative analysis between them.
  }
}