Edge detection is a critical component of many vision systems, including object detectors and image segmentation algorithms. Patches of edges exhibit well-known forms of local structure, such as straight lines or T-junctions. In this paper we take advantage of the structure present in local image patches to learn both an accurate and computationally efficient edge detector. We formulate the problem of predicting local edge masks in a structured learning framework applied to random decision forests. Our novel approach to learning decision trees robustly maps the structured labels to a discrete space on which standard information gain measures may be evaluated. The result is an approach that obtains realtime performance that is orders of magnitude faster than many competing state-of-the-art approaches, while also achieving state-of-the-art edge detection results on the BSDS500 Segmentation dataset and NYU Depth dataset. Finally, we show the potential of our approach as a general purpose edge detector by showing our learned edge models generalize well across datasets.

本文提出了一种借助于本地图像块中的结构学习精确且高效的边缘检测器。基于随机决策森林的结构学习框架，本文的新方法将结构标签映射到离散空间上。利用该方法，在BSDS500分割数据集和NYU深度数据集上实现了最先进的边缘检测表现，同时达到了比许多竞争对手先进得多的实时性能，证明该方法具备通用性和实用性。

使用结构化森林实现快速边缘检测