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Window Detection from Terrestrial Laser Scanner Data - A Statistical Approach

This paper proposes a window detection system using applied statistics and image based methods from Terrestrial Laser Scanners which can be used for direct application in a deformation measurement system. It exploits the laser distance information either directly in the laser scanner spherical coordinate space images, or on segmented planar facade patches, both with the assumption that the laser beam penetrates windows. The applied statistical method uses basic local features on local distance variations and decides on an adaptive threshold on the basis of the 1− Sigma percentile upper limit with P90 90% and P10 10% produced sample quartiles of the data for the laser spherical coordinate system image and Q3 − Sigma for the ortho images of segmented 3D facade planes as a location in the order statistics. For window detection the image is binarized and morphological closing is performed using the derived adaptive threshold. Thereafter we do the contour analysis and obtain the bounding rectangles positions that directly form the window segments in the image. We compare the window detection results on the laser spherical coordinate system image with those on ortho images of segmented 3D facades. The system provides a windows detection rate of more than 85% with a processing time of less than a minute in a typical 360 degree laser scan image.