This paper presents a fast and accurate edge detection method using structured forests. The authors propose a structured learning approach that leverages the local structure of image patches to learn an efficient edge detector. The method formulates edge detection as a structured learning problem, where decision trees are trained to predict local edge masks. The structured labels are mapped to a discrete space, enabling the use of standard information gain measures. The approach achieves real-time performance, significantly faster than many state-of-the-art methods, while maintaining high accuracy on benchmark datasets such as BSDS500 and NYU Depth. The method uses random decision forests to capture the structured nature of edge patches. The structured labels are mapped to a discrete space, allowing the use of standard information gain measures. The approach is efficient and robust, with the ability to generalize across different datasets. The authors also introduce enhancements such as multiscale detection and edge sharpening to further improve performance. The paper evaluates the method on the BSDS500 and NYU Depth datasets, demonstrating superior accuracy and efficiency compared to existing methods. The structured edge detector achieves state-of-the-art results on these datasets, with the SE+SH variant showing the best performance in terms of accuracy and speed. The method is also shown to generalize well across different datasets, making it a versatile edge detection tool. The approach is based on structured learning, which addresses the problem of learning mappings where the input or output space may be complex. The structured random forests differ from previous works in that they assume a structured output space but operate on a standard input space. The method uses a two-stage approach to map structured labels to a discrete space, enabling efficient training and inference. The structured random forests are trained using a combination of feature extraction and information gain criteria, leading to accurate and efficient edge detection. The paper also discusses related work in edge detection and structured learning, highlighting the importance of structured learning in capturing the complex relationships between image patches. The authors compare their method to existing approaches, showing that their structured edge detector achieves better accuracy and efficiency. The method is particularly effective in the high recall regime, which is important for many vision tasks. Overall, the paper presents a novel and efficient approach to edge detection using structured forests, demonstrating its effectiveness on benchmark datasets and its potential as a general-purpose edge detection tool. The method is fast, accurate, and robust, making it suitable for a wide range of applications in computer vision.This paper presents a fast and accurate edge detection method using structured forests. The authors propose a structured learning approach that leverages the local structure of image patches to learn an efficient edge detector. The method formulates edge detection as a structured learning problem, where decision trees are trained to predict local edge masks. The structured labels are mapped to a discrete space, enabling the use of standard information gain measures. The approach achieves real-time performance, significantly faster than many state-of-the-art methods, while maintaining high accuracy on benchmark datasets such as BSDS500 and NYU Depth. The method uses random decision forests to capture the structured nature of edge patches. The structured labels are mapped to a discrete space, allowing the use of standard information gain measures. The approach is efficient and robust, with the ability to generalize across different datasets. The authors also introduce enhancements such as multiscale detection and edge sharpening to further improve performance. The paper evaluates the method on the BSDS500 and NYU Depth datasets, demonstrating superior accuracy and efficiency compared to existing methods. The structured edge detector achieves state-of-the-art results on these datasets, with the SE+SH variant showing the best performance in terms of accuracy and speed. The method is also shown to generalize well across different datasets, making it a versatile edge detection tool. The approach is based on structured learning, which addresses the problem of learning mappings where the input or output space may be complex. The structured random forests differ from previous works in that they assume a structured output space but operate on a standard input space. The method uses a two-stage approach to map structured labels to a discrete space, enabling efficient training and inference. The structured random forests are trained using a combination of feature extraction and information gain criteria, leading to accurate and efficient edge detection. The paper also discusses related work in edge detection and structured learning, highlighting the importance of structured learning in capturing the complex relationships between image patches. The authors compare their method to existing approaches, showing that their structured edge detector achieves better accuracy and efficiency. The method is particularly effective in the high recall regime, which is important for many vision tasks. Overall, the paper presents a novel and efficient approach to edge detection using structured forests, demonstrating its effectiveness on benchmark datasets and its potential as a general-purpose edge detection tool. The method is fast, accurate, and robust, making it suitable for a wide range of applications in computer vision.