Lluis Pere de las Heras author David Fernandez author Ernest Valveny author Josep Llados author Gemma Sanchez author 2013 Wall detection in floor plans is a crucial step in a complete floor plan recognition system. Walls define the main structure of buildings and convey essential information for the detection of other structural elements. Nevertheless, wall segmentation is a difficult task, mainly because of the lack of a standard graphical notation. The existing approaches are restricted to small group of similar notations or require the existence of pre-annotated corpus of input images to learn each new notation. In this paper we present an automatic wall segmentation system, with the ability to handle completely different notations without the need of any annotated dataset. It only takes advantage of the general knowledge that walls are a repetitive element, naturally distributed within the plan and commonly modeled by straight parallel lines. The method has been tested on four datasets of real floor plans with different notations, and compared with the state-of-the-art. The results show its suitability for different graphical notations, achieving higher recall rates than the rest of the methods while keeping a high average precision. DAG; 600.061; 600.056; 600.045 exported from refbase (http://refbase.cvc.uab.es/show.php?record=2319), last updated on Thu, 10 Nov 2016 12:11:13 +0100 text http://refbase.cvc.uab.es/files/HFV2013.pdf 10.1109/ICDAR.2013.252 Admin @ si @ HFV2013 ICDAR 2013 conference publication 1245 1249 1520-5363