Efficient Filtering for Edge Extraction under Perspective Effect

Abstract

Though it is generally believed that edges should be extracted at different scales when using a linear filter, it is still difficult to determine the optimal scale for each filter. In this paper, we propose a novel approach called orientation and scale tuned difference of boxes (osDoB) to solve this problem. For certain computer vision applications, such as lane marking detection, the prior information about the concerned target can facilitate edge extraction in a top-down manner. Based on the perspective effect, we associate the scale of the edge in an image with the target size in the real world and assign orientation and scale parameters for filtering each pixel. Considering the fact that it is very time-consuming to naïvely perform filters with different orientations and scales, we further design an extended integration map technology to speed up filtering. Our method is validated on synthetic and real data. The experimental results show that assigning appropriate orientation and scale parameters for filters is effective and can be realized efficiently.