Automatic Kronecker Product Model Based Detection of Repeated Patterns in 2D Urban Images

Abstract

Repeated patterns (such as windows, tiles, balconies and doors) are prominent and significant features in urban scenes. Therefore, detection of these repeated patterns becomes very important for city scene analysis. This paper attacks the problem of repeated patterns detection in a precise, efficient and automatic way, by combining traditional feature extraction followed by a Kronecker product lowrank modeling approach. Our method is tailored for 2D images of building fac,ades. We have developed algorithms for automatic selection of a representative texture within fac,ade images using vanishing points and Harris corners. After rectifying the input images, we describe novel algorithms that extract repeated patterns by using Kronecker product based modeling that is based on a solid theoretical foundation. Our approach is unique and has not ever been used for fac,ade analysis. We have tested our algorithms in a large set of images.