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Improved Contour-Based Corner Detection for Architectural Floor Plans

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Graphics Recognition. Current Trends and Challenges (GREC 2013)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8746))

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Abstract

A new rotation invariant corner detection method for architectural line drawing images is proposed in this paper. The proposed method is capable of finding corners of objects in line drawing images by filtering out unnecessary points without changing the overall structure. Especially, in case of diagonal lines and corners, our method is capable of removing repetitive points. The proposed method is applied to corner detection of walls in floor plans which in turn are used for detection of wall edges. To evaluate the effectiveness of detected corners, gap closing and wall edge detection is performed on a publicly available dataset of 90 floor plans, where we achieved a recognition and detection accuracy of 95 %.

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Notes

  1. 1.

    The actual image size is \(2479 * 3508\). For making the analysis process more efficient, isotropic down scaling to \(1413 * 2000\) has been applied.

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Author information

Authors and Affiliations

  1. University of Kaiserslautern, Kaiserslautern, Germany

    Max Feltes, Sheraz Ahmed & Andreas Dengel

  2. German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany

    Sheraz Ahmed, Andreas Dengel & Marcus Liwicki

  3. University of Fribourg, Fribourg, Switzerland

    Marcus Liwicki

Authors
  1. Max Feltes
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  2. Sheraz Ahmed
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  3. Andreas Dengel
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  4. Marcus Liwicki
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Corresponding author

Correspondence to Sheraz Ahmed .

Editor information

Editors and Affiliations

  1. Université de Lorraine, Vandoeuvre-lès-Nancy, France

    Bart Lamiroy

  2. Université de La Rochelle, La Rochelle, France

    Jean-Marc Ogier

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Feltes, M., Ahmed, S., Dengel, A., Liwicki, M. (2014). Improved Contour-Based Corner Detection for Architectural Floor Plans. In: Lamiroy, B., Ogier, JM. (eds) Graphics Recognition. Current Trends and Challenges. GREC 2013. Lecture Notes in Computer Science(), vol 8746. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44854-0_15

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  • DOI: https://doi.org/10.1007/978-3-662-44854-0_15

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  • Print ISBN: 978-3-662-44853-3

  • Online ISBN: 978-3-662-44854-0

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