Unsupervised and notation-independent wall segmentation in floor plans using a combination of statistical and structural strategies

Lluís Pere de las Heras, Ernest Valveny, Gemma Sánchez

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1 Citation (Scopus)

Abstract

© Springer-Verlag Berlin Heidelberg 2014. 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.
Original languageEnglish
Pages (from-to)109-121
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8746
DOIs
Publication statusPublished - 1 Jan 2014

Keywords

  • Floor plan analysis
  • Graphics recognition
  • Object segmentation

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    de las Heras, L. P., Valveny, E., & Sánchez, G. (2014). Unsupervised and notation-independent wall segmentation in floor plans using a combination of statistical and structural strategies. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8746, 109-121. https://doi.org/10.1007/978-3-662-44854-0_9