第一阶段是一个分割网络,实现缺陷的像素级定位;第二阶段是二分类网络,使用分割网络的feature map 和分割网络的输出进行分类。(segmentation network +decision network) image.png 2.为实现大分辨率图像上的小缺陷检测任务,网络结构设计需要具有如下特点,1)大感受野,即大的卷积核2)获取小尺寸特征细节的能力。 segmentat...
and T.-S. Kim, “A fully integrated computer-aided diagnosis system for digital x-ray mammograms via deep learning detection, segmentation, and classification,” Int. J. Med. Inform., vol. 117, pp. 44–54, 2018.
Each RefineNet block has a component to fuse the multi resolution features by upsampling the lower resolution features and a component to capture context based on repeated 5 x 5stride 1pool layers. Each of these components employ the residual connection design following the identity map mindset. 6...
* 题目: AutoDepthNet: High Frame Rate Depth Map Reconstruction using Commodity Depth and RGB Cameras* PDF: arxiv.org/abs/2305.1473* 作者: Peyman Gholami,Robert Xiao 三维视觉-位姿估计 2篇 * 题目: Dual-Side Feature Fusion 3D Pose Transfer* PDF: arxiv.org/abs/2305.1495* 作者: Jue Liu,Feipe...
Additionally, inspired by the ResNet32 approach, we introduced additional skip connections within every set of convolutional layers (which we call a U-Net block) before pooling, where the input of the U-Net block is concatenated with the last feature map produced by the block itself. This ...
Notations in blue text (a × a × a × b) highlight the spatial resolution (a × a × a) and the feature map count (b). X block repetitions, IN instance normalization, Conv convolution kernel, ReLU rectified linear unit, 3 × 3 × 3 the size of the 3D CNN kernels. (...
DFANet 全称 DFANet: Deep Feature Aggregation for Real-Time Semantic Segmentation。DFANet 与 ICNet 形式上很像,区别在于利用多尺度融合特征来替换多尺度图片输入(sub-network Aggregation),不同支路之间的 feature map 之间也加上短连接来增强特征融合(sub-stage Aggregation)。
In most of the cases, graphical models are applied on top of the likelihood map produced by CNNs or fCNNs and act as label regularizers. Summarizing, segmentation in medical imaging has seen a huge influx of deep learning related methods. Custom architectures have been created to directly ...
* 题目: Poses as Queries: Image-to-LiDAR Map Localization with Transformers* PDF: arxiv.org/abs/2305.0429* 作者: Jinyu Miao,Kun Jiang,Yunlong Wang,Tuopu Wen,Zhongyang Xiao,Zheng Fu,Mengmeng Yang,Maolin Liu,Diange Yang* 其他: 8 pages, 3 figures, 4 tables* 题目: Design, Implementation and...
在这项工作中,我们将停留点集中在带有DBSCAN的轨迹数据集中,并消除冗余数据,以通过降低处理时间来提高MAP匹配算法的效率。与基于模糊逻辑的地图匹配算法相比,我们认为我们提出的方法的性能和精确性。幸运的是,我们的方法可产生27.39%的数据尺寸减少和8.9%的处理时间缩短,其准确结果与以前的基于模糊的MAP匹配方法相同。