Automated Extraction of Photorealistic Facade Textures from Single Ground-Level Building Images

Abstract

An integrated approach is presented for the automatic extraction of photorealistic facade textures from single street-level building images. The initial facade texture is extracted using Watershed segmentation. The seed pixels (markers) to trigger the segmentation are located automatically both for the foreground (facade) and the background regions, and the segmentation is carried out repetitively until the facade texture is extracted. The extracted facade image is geometrically rectified using a developed automatic technique based on Hough transformation and interest point detection. The occluded areas on facade textures are restored by employing an image matching-based procedure. The approach was tested on two different datasets captured from the residential areas of Ankara, the capital of Turkey. The datasets contain a total of 40 building facade images that were taken from the street-level. The results indicate that the facade textures are extracted adequately. For facade image extraction, an average quantitative accuracy of 83% was achieved. For rectification, 24 out of 40 buildings provided the positional error under 10 pixels at 95% confidence level. The subjective assessment of facade restoration yielded the mean rating value of 2.46 for the datasets used, in which the rating values are ranked between 1 for "Excellent" and 6 for "Unusable".