- 标题
- 摘要
- 关键词
- 实验方案
- 产品
-
[IEEE 2018 IEEE International Conference on Imaging Systems and Techniques (IST) - Krakow (2018.10.16-2018.10.18)] 2018 IEEE International Conference on Imaging Systems and Techniques (IST) - Main Aortic Segmentation from CTA with Deep Feature Aggregation Network
摘要： In this study, we propose a Deep Feature Aggregation network (DFA-Net) for main aortic segmentation from CTA(Computed Tomography Angiography) by aggregating features from forwarding layers to leverage more visual information. To practically verify the effectiveness of our method, we collect 90 CTA volumes from Beijing AnZhen Hospital up to over 60 thousands 2-D slices. First, we use a level-set based algorithm to efficiently generate the dataset for training and validating the deep model. Then the dataset is divided into three parts, 70 instances are used for training and 5 instances are used for validating the best parameters, and the rest 15 instances are used for testing the generalization of the model. Finally, the testing result shows that mIoU(mean Intersection-over-Union) of the segmentation result is 0.943, which indicates that by properly aggregating more visual features in a deep network the segmentation model can achieve state-of-the-art performance.
关键词： CTA,feature aggregation,level set,main aortic segmentation
更新于2025-09-23 15:22:29
-
[IEEE 2018 24th International Conference on Pattern Recognition (ICPR) - Beijing, China (2018.8.20-2018.8.24)] 2018 24th International Conference on Pattern Recognition (ICPR) - 3DMAX-Net: A Multi-Scale Spatial Contextual Network for 3D Point Cloud Semantic Segmentation
摘要： Semantic segmentation of 3D scenes is a fundamental problem in 3D computer vision. In this paper, we propose a deep neural network for 3D semantic segmentation of raw point clouds. A multi-scale feature learning block is first introduced to obtain informative contextual features in 3D point clouds. A global and local feature aggregation block is then extended to improve the feature learning ability of the network. Based on these strategies, a powerful architecture named 3DMAX-Net is finally provided for semantic segmentation in raw 3D point clouds. Experiments have been conducted on the Stanford large-scale 3D Indoor Spaces Dataset using only geometry information. Experimental results have clearly shown the superiority of the proposed network.
关键词： 3D semantic segmentation,point clouds,multi-scale feature learning,feature aggregation,deep learning
更新于2025-09-09 09:28:46
-
[IEEE 2018 24th International Conference on Pattern Recognition (ICPR) - Beijing, China (2018.8.20-2018.8.24)] 2018 24th International Conference on Pattern Recognition (ICPR) - Multi-layer CNN Features Aggregation for Real-time Visual Tracking
摘要： In this paper, we propose a novel convolutional neural network (CNN) based tracking framework, which aggregates multiple CNN features from different layers into a robust representation and realizes real-time tracking. We found that some feature maps have interference for effectively representing objects. Instead of using original features, we build an end-to-end feature aggregation network (FAN) which suppresses the noisy feature maps of CNN layers. The feature significantly benefits to represent objects with both coarse semantic information and fine details. The FAN, as a light-weight network, can run at real-time. The highlighted region of feature maps obtained from the FAN is the tracking result. Our method performs at a real-time speed of 24 fps while maintaining a promising accuracy compared with state-of-the-art methods on existing tracking benchmarks.
关键词： real-time tracking,convolutional neural network,feature aggregation,visual tracking
更新于2025-09-04 15:30:14