MIND YOUR GREY TONES – EXAMINING THE INFLUENCE OF DECOLOURIZATION METHODS ON INTEREST POINT EXTRACTION AND MATCHING FOR ARCHITECTURAL IMAGE-BASED MODELLING
- 1Ludwig Boltzmann Institute for Archaeological Prospection & Virtual Archaeology (LBI ArchPro), Hohe Warte 38, 1190 Vienna, Austria
- 2Department of Geodesy and Geoinformation, Vienna University of Technology, Gusshausstrasse 27-29, 1040 Vienna, Austria
- 3Vienna Institute for Archaeological Science (VIAS), University of Vienna, Franz-Klein-Gasse 1, 1190 Vienna, Austria
- 4University of Ljubljana, Faculty of Arts, Aškerčeva 2, 1000 Ljubljana, Slovenia
- 5Department of Prehistoric and Historical Archaeology, University of Vienna, Franz-Klein-Gasse 1, 1190 Vienna, Austria
Keywords: Architecture, Colour, Decolourization, Feature, Imagery, Modelling, Point Cloud, Structure-from-Motion
Abstract. This paper investigates the use of different greyscale conversion algorithms to decolourize colour images as input for two Structure-from-Motion (SfM) software packages. Although SfM software commonly works with a wide variety of frame imagery (old and new, colour and greyscale, airborne and terrestrial, large-and small scale), most programs internally convert the source imagery to single-band, greyscale images. This conversion is often assumed to have little, if any, impact on the final outcome.
To verify this assumption, this article compares the output of an academic and a commercial SfM software package using seven different collections of architectural images. Besides the conventional 8-bit true-colour JPEG images with embedded sRGB colour profiles, for each of those datasets, 57 greyscale variants were computed with different colour-to-greyscale algorithms. The success rate of specific colour conversion approaches can therefore be compared with the commonly implemented colour-to-greyscale algorithms (luma Y’601, luma Y’709, or luminance CIE Y), both in terms of the applied feature extractor as well as of the specific image content (as exemplified by the two different feature descriptors and the various image collections, respectively).
Although the differences can be small, the results clearly indicate that certain colour-to-greyscale conversion algorithms in an SfM-workflow constantly perform better than others. Overall, one of the best performing decolourization algorithms turns out to be a newly developed one.