Windows Detection Using K-means in CIE-Lab Color Space

Abstract

In this paper, we present a method for window detection, robust enough to process complex façades of historical buildings. This method is able to provide results even for facades under severe perspective distortion. Our algorithm is able to detect many different window types and does not require a learning step. We achieve these features thanks to an extended gradient projection method and introduction of a façade color descriptor based on a k-means clustering in a CIE-Lab color space into the process. This method is an important step towards creating large 3D city models in an automated workflow from large online image databases, or industrial systems. As such, it was designed to provide a high level of robustness for processing a large variety of façade types.