Severe weather conditions cause enormous amount of damages around the globe. Bow echo patterns in radar images are associated with a number of these destructive thunderstorm conditions such as damaging winds, hail and tornadoes. They are detected manually by meteorologists. In this paper, we propose an automatic framework to detect these patterns with high accuracy by introducing novel skeletonization and shape matching approaches. In this framework, first we extract regions with high probability of occurring bow echo from radar images, and apply our skeletonization method to extract the skeleton of those regions. Next, we prune these skeletons using our innovative pruning scheme with fuzzy logic. Then, using our proposed shape descriptor, Skeleton Context, we can extract bow echo features from these skeletons in order to use them in shape matching algorithm and classification step. The output of classification indicates whether these regions include a bow echo with over 97% accuracy.
Linear perspective is widely used in landscape photography to create the impression of depth on a 2D photo. Automated understanding of the use of linear perspective in landscape photography has a number of real-world applications, including aesthetics assessment, image retrieval, and on-site feedback for photo composition. We address this problem by detecting vanishing points and the associated line structures in photos. However, natural landscape scenes pose great technical challenges because there are often inadequate number of strong edges converging to the vanishing points. To overcome this difficulty, we propose a novel vanishing point detection method that exploits global structures in the scene via contour detection. We show that our method significantly outperforms state-of-the-art methods on a public ground truth landscape image dataset that we have created. Based on the detection results, we further demonstrate how our approach to linear perspective understanding can be used to provide on-site guidance to amateur photographers on their work through a novel viewpoint-specific image retrieval system.