@TechReport{TR106,
  author =	 "Thomas Illetschko and Adrian Ion and Yll Haxhimusa
                  and Walter G. Kropatsch",
  title =	 "Effective Programming of Combinatorial Maps using
                  COMA - A C++ Framework for Combinatorial Maps",
  institution =	 "PRIP, TU Wien",
  number =	 "PRIP-TR-106",
  year =	 "2006",
  url =		 "https://www.prip.tuwien.ac.at/pripfiles/trs/tr106.pdf",
  abstract =	 "Combinatorial maps and pyramids have been studied in
                  great detail in the past, and it has been shown that
                  this concept is advantageous for many applications
                  in the field of image processing and pattern
                  recognition by providing means to store information
                  of the topological relations of the represented
                  data. In the course of these studies, the properties
                  of combinatorial maps have been investigated using
                  different sets of permutations, different operations
                  and different algorithms. In each case new software
                  had to be created in order to conduct experiments,
                  as the existing programs were designed to work only
                  for a specific model. Due to the complexity of
                  combinatorial maps, the implementation of such a
                  software is a time and resource intensive task. Thus
                  these programming efforts were often responsible for
                  delaying the presentation of new results in the
                  past. This paper presents COMA - a C++ framework for
                  combinatorial maps - that has been created during
                  recent studies of combinatorial maps, motivated by
                  this problem. Using an object oriented approach,
                  COMA was specifically designed to allow an efficient
                  and quick integration of changes to the model of
                  combinatorial maps used, as well as the
                  implementation of new algorithms. As a consequence
                  COMA significantly reduces the amount of time needed
                  to set up new experiments.",
}