Fast Edge Detection Using Structured Forests
Fast Edge Detection Using Structured Forests
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DOI：
10.1109/tpami.2014.2377715
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发表时间：
2015-08-01
影响因子：
23.6
通讯作者：
Zitnick, C. Lawrence
中科院分区：
文献类型：
--
作者：
Dollar, Piotr;Zitnick, C. Lawrence
边缘检测是许多视觉系统的重要组成部分，包括目标检测器和图像分割算法。片状的边缘表现出众所周知的局部结构形式，如直线或T形连接。在本文中，我们利用局部图像块中存在的结构来学习一种既准确又计算高效的边缘检测器。我们提出了一种适用于随机决策森林的结构化学习框架中的局部边缘掩码预测问题。我们的学习决策树的新方法将结构化标签稳健地映射到离散空间，在该空间上可以评估标准信息增益度量。其结果是，该方法获得的实时性能比许多竞争最先进的方法快了一个数量级，同时还在BSDS500分段数据集和NYU深度数据集上获得了最先进的边缘检测结果。最后，我们展示了我们的方法作为一个通用边缘检测器的潜力，通过展示我们的学习边缘模型很好地在数据集上泛化。
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.