Detecting repetitive elements with accurate locations and shapes from urban façade

This paper proposes a novel algorithm to automatically detect the repetitive elements with accurate shapes, locations and sizes from single façade image. Unlike other algorithms, our algorithm is not entirely dependent on the extracted feature points, edges and symmetric information. Our algorithm mainly includes following steps: First, we combine the clustering method with the repetitive characteristic curve to derive templates and to detect repetitive elements matched with derived templates. Moreover, a global repetition-based optimization framework is proposed to derive occluded repetitive elements and determine the number of all the repetitive elements with the accurate locations, shapes and sizes. Experiment results demonstrate that the proposed algorithm improves the accuracy, robustness and efficiency on façade databases compared with the state-of-the-art methods.