Event Details
Geometry-based Symbol Spotting in Digital Architectural Floor Plans
Presenter: Alireza Rezvanifar
Supervisor:
Date: Fri, August 20, 2021
Time: 10:00:00 - 00:00:00
Place: ZOOM - Please see below.
ABSTRACT
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Link: https://uvic.zoom.us/j/82374639690?pwd=eTB4WkJhenBTMzV2V2tZK3BwT1I5Zz09
Meeting ID: 823 7463 9690
Password: 085955
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Abstract:
The analysis of digital graphics-rich documents such as architectural floor plans focuses on the automatic extraction of visual information initially intended for human comprehension. In particular, symbol spotting aims to detect a ranked list of regions of interest which are likely to contain a query symbol. The main requirement for symbol spotting relates to performing on-the-fly queries, which precludes us from using learning-based methods, including deep learning. In this work, we introduce a hybrid method that capitalizes on strengths of both vector-based and pixel-based symbol spotting techniques. In the description phase, the salient geometric constituents of a symbol are extracted by a variety of vectorization techniques, including a proposed voting-based algorithm for finding partial ellipses. In the matching phase, the spatial relationship between the geometric primitives is encoded via a primitive-aware proximity graph. A statistical approach is then used to rapidly yield a coarse localization of symbols within the plan. Localization is further refined with a pixel-based step implementing a modified cross-correlation function. Experimental results on the public SESYD synthetic dataset demonstrate that our approach clearly outperforms other popular symbol spotting approaches. Moreover, our approach yields promising results on real-world images, which are significantly more challenging in terms of overlap, occlusion and presence of non-symbolic graphical data.