结构化森林快速边缘检测

Piotr Dollár, C. L. Zitnick
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引用次数: 934

摘要

边缘检测是许多视觉系统的关键组成部分,包括目标检测器和图像分割算法。一块块的边缘呈现出众所周知的局部结构形式,如直线或t形结。在本文中,我们利用局部图像补丁中存在的结构来学习精确且计算效率高的边缘检测器。我们在一个应用于随机决策森林的结构化学习框架中提出了预测局部边缘掩码的问题。我们的学习决策树的新方法鲁棒地将结构化标签映射到一个可以评估标准信息增益度量的离散空间。结果是一种获得实时性能的方法,比许多竞争的最先进的方法快几个数量级,同时在BSDS500分割数据集和NYU深度数据集上也获得了最先进的边缘检测结果。最后,我们通过展示我们学习的边缘模型在数据集上的良好泛化,展示了我们的方法作为通用边缘检测器的潜力。
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Structured Forests for Fast Edge Detection
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 real time 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.
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