Volume XLII-2
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 643-650, 2018
https://doi.org/10.5194/isprs-archives-XLII-2-643-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 643-650, 2018
https://doi.org/10.5194/isprs-archives-XLII-2-643-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  30 May 2018

30 May 2018

FEATURE MATCHING OF HISTORICAL IMAGES BASED ON GEOMETRY OF QUADRILATERALS

F. Maiwald1, D. Schneider1, F. Henze1, S. Münster2, and F. Niebling3 F. Maiwald et al.
  • 1Institute of Photogrammetry and Remote Sensing, TU Dresden, Germany
  • 2Media Center, TU Dresden, Germany
  • 3Human-Computer Interaction, Julius-Maximilians-Universität Würzburg, Germany

Keywords: historical images, image orientation, feature matching, descriptor matching, quadrilaterals, geometry

Abstract. This contribution shows an approach to match historical images from the photo library of the Saxon State and University Library Dresden (SLUB) in the context of a historical three-dimensional city model of Dresden. In comparison to recent images, historical photography provides diverse factors which make an automatical image analysis (feature detection, feature matching and relative orientation of images) difficult. Due to e.g. film grain, dust particles or the digitalization process, historical images are often covered by noise interfering with the image signal needed for a robust feature matching. The presented approach uses quadrilaterals in image space as these are commonly available in man-made structures and façade images (windows, stones, claddings). It is explained how to generally detect quadrilaterals in images. Consequently, the properties of the quadrilaterals as well as the relationship to neighbouring quadrilaterals are used for the description and matching of feature points. The results show that most of the matches are robust and correct but still small in numbers.