TY  - CHAP
AU  - Lluis Pere de las Heras
AU  - Ernest Valveny
AU  - Gemma Sanchez
PY  - 2014//
TI  - Unsupervised and Notation-Independent Wall Segmentation in Floor Plans Using a Combination of Statistical and Structural Strategies
T2  - LNCS
BT  - Graphics Recognition. Current Trends and Challenges
SP  - 109
EP  - 121
VL  - 8746
PB  - Springer Berlin Heidelberg
KW  - Graphics recognition
KW  - Floor plan analysis
KW  - Object segmentation
N2  - In this paper we present a wall segmentation approach in floor plans that is able to work independently to the graphical notation, does not need any pre-annotated data for learning, and is able to segment multiple-shaped walls such as beams and curved-walls. This method results from the combination of the wall segmentation approaches [3, 5] presented recently by the authors. Firstly, potential straight wall segments are extracted in an unsupervised way similar to [3], but restricting even more the wall candidates considered in the original approach. Then, based on [5], these segments are used to learn the texture pattern of walls and spot the lost instances. The presented combination of both methods has been tested on 4 available datasets with different notations and compared qualitatively and quantitatively to the state-of-the-art applied on these collections. Additionally, some qualitative results on floor plans directly downloaded from the Internet are reported in the paper. The overall performance of the method demonstrates either its adaptability to different wall notations and shapes, and to document qualities and resolutions.
SN  - 0302-9743
SN  - 978-3-662-44853-3
UR  - http://dx.doi.org/10.1007/978-3-662-44854-0_9
N1  - DAG; ADAS; 600.076; 600.077
ID  - Lluis Pere de las Heras2014
ER  -