Partitioning Open Plan Areas in Floor Plans


Journal article


Anuradha Madugalla, K. Marriott, S. Marinai
IEEE International Conference on Document Analysis and Recognition, 2017

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APA   Click to copy
Madugalla, A., Marriott, K., & Marinai, S. (2017). Partitioning Open Plan Areas in Floor Plans. IEEE International Conference on Document Analysis and Recognition.


Chicago/Turabian   Click to copy
Madugalla, Anuradha, K. Marriott, and S. Marinai. “Partitioning Open Plan Areas in Floor Plans.” IEEE International Conference on Document Analysis and Recognition (2017).


MLA   Click to copy
Madugalla, Anuradha, et al. “Partitioning Open Plan Areas in Floor Plans.” IEEE International Conference on Document Analysis and Recognition, 2017.


BibTeX   Click to copy

@article{anuradha2017a,
  title = {Partitioning Open Plan Areas in Floor Plans},
  year = {2017},
  journal = {IEEE International Conference on Document Analysis and Recognition},
  author = {Madugalla, Anuradha and Marriott, K. and Marinai, S.}
}

Abstract

We are developing an application to automatically generate an accessible graphic from a floor plan image. Floor plans generally contain large regions with functionally different sub-areas. A problem faced by visually impaired users in exploring such accessible floor plans is understanding the boundaries of these sub-areas. We present an effective method to partition such open plan areas. Initially, we conducted a formative user study to understand how people partition open plan areas. Based on the findings of the study, we identified a general set of guidelines for partitioning open plans. These guidelines were used to generate a set of candidate lines for sub-areas. An obstacle avoiding shortest-path Voronoi diagram was used to determine boundaries for each sub-area. Candidate lines such as wall extensions were automatically generated to replace the identified boundaries. We selected the best replacement for each boundary by scoring candidate lines using a set of criteria such as line length. Finally the proposed method was tested on a standard floor plan corpus using three novel measures.


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