%0 Conference Proceedings
%T Combining structural and statistical strategies for unsupervised wall detection in floor plans
%A Lluis Pere de las Heras
%A Ernest Valveny
%A Gemma Sanchez
%B 10th IAPR International Workshop on Graphics Recognition
%D 2013
%F Lluis Pere de las Heras2013
%O DAG; 600.045
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=2321), last updated on Thu, 10 Nov 2016 12:11:28 +0100
%X This paper presents an evolution of the first unsupervised wall segmentation method in floor plans, that was presented by the authors in [1]. This first approach, contrarily to the existing ones, is able to segment walls independently to their notation and without the need of any pre-annotated datato learn their visual appearance. Despite the good performance of the first approach, some specific cases, such as curved shaped walls, were not correctly segmented since they do not agree the strict structural assumptions that guide the whole methodology in order to be able to learn, in an unsupervised way, the structure of a wall. In this paper, we refine this strategy by dividing theprocess in two steps. In a first step, potential wall segments are extracted unsupervisedly using a modification of [1], by restricting even more the areas considered as walls in a first moment. In a second step, these segments are used to learn and spot lost instances based on a modified version of [2], also presented by the authors. The presented combined method have been tested on4 datasets with different notations and compared with the stateof-the-art applyed on the same datasets. The results show its adaptability to different wall notations and shapes, significantly outperforming the original approach.
%U http://refbase.cvc.uab.es/files/HVS2013.pdf