Holistically-Nested Edge Detection
We develop a new edge detection algorithm that tackles two important issues in this long-standing vision problem: (1) holistic image training and prediction; and (2) multi-scale and multi-level feature learning. Our proposed method, holistically-nested edge detection (HED), performs image-to-image prediction by means of a deep learning model that leverages fully convolutional neural networks and deeply-supervised nets. HED automatically learns rich hierarchical representations (guided by deep supervision on side responses) that are important in order to approach the human ability resolve the challenging ambiguity in edge and object boundary detection. We significantly advance the state-of-the-art on the BSD500 dataset (ODS F-score of .782) and the NYU Depth dataset (ODS F-score of .746), and do so with an improved speed (0.4 second per image) that is orders of magnitude faster than some recent CNN-based edge detection algorithms.
Code (17)
Tasks
Boundary DetectionEdge DetectionSimilar Papers 제목 키워드 기반
Dense Extreme Inception Network: Towards a Robust CNN Model for Edge Detection
This paper proposes a Deep Learning based edge detector, which is inspired on both HED (Holistically-Nested Edge Detection) and Xception networks. The proposed approach generates thin edge-maps that are plausible for hum…
Boundary DetectionEdge DetectionEnhancing Transparent Object Pose Estimation: A Fusion of GDR-Net and Edge Detection
Object pose estimation of transparent objects remains a challenging task in the field of robot vision due to the immense influence of lighting, background, and reflections. However, the edges of clear objects have the hi…
6D Pose Estimation using RGBEdge DetectionObjectobject-detection+3Deeply supervised salient object detection with short connections
Recent progress on saliency detection is substantial, benefiting mostly from the explosive development of Convolutional Neural Networks (CNNs). Semantic segmentation and saliency detection algorithms developed lately hav…
Boundary DetectionObjectobject-detectionObject Detection+4HA U-Net: Improved Model for Building Extraction From High Resolution Remote Sensing Imagery
Deep learning , building extraction , holistically-nested neural network , attention mechanism , weight mapping , watershed algorithm
Deep LearningDoubly Nested Network for Resource-Efficient Inference
We propose doubly nested network(DNNet) where all neurons represent their own sub-models that solve the same task. Every sub-model is nested both layer-wise and channel-wise. While nesting sub-models layer-wise is straig…