机构

• 金属研究所 [1]
• 地理科学与资源研究所 [1]
• 长春光学精密机械与物... [1]
• 自动化研究所 [1]
• 沈阳自动化研究所 [1]

采集方式

• OAI收割 [5]

内容类型

• 期刊论文 [4]
• 会议论文 [1]

发表日期

• 2023 [2]
• 2018 [2]
• 2011 [1]

学科主题

筛选

浏览/检索结果: 共5条，第1-5条 帮助 条数/页： 5101520253035404550556065707580859095100 排序方式： 请选择题名升序题名降序提交时间升序提交时间降序作者升序作者降序发表日期升序发表日期降序 Underwater Structured Light Stripe Center Extraction with Normalized Grayscale Gravity Method 期刊论文  OAI收割 SENSORS, 2023, 卷号: 23, 期号: 24, 页码: 18 作者:  Li, Shuaishuai;  Gao, Xiang;  Xie, Zexiao   |  收藏  |  浏览/下载：101/0  |  提交时间：2024/02/22 light stripe center extraction  underwater measurement  underwater semantic segmentation  normalized grayscale gravity method   Identifying Tree Species in a Warm-Temperate Deciduous Forest by Combining Multi-Rotor and Fixed-Wing Unmanned Aerial Vehicles 期刊论文  OAI收割 DRONES, 2023, 卷号: 7, 期号: 6, 页码: 353 作者:  Shi, Weibo;  Wang, Shaoqiang;  Yue, Huanyin;  Wang, Dongliang;  Ye, Huping   |  收藏  |  浏览/下载：19/0  |  提交时间：2023/08/22 fixed-wing UAVs  multi-rotor UAVs  semantic segmentation method  tree species classification  forest inventory   Methods and datasets on semantic segmentation: A review 期刊论文  OAI收割 NEUROCOMPUTING, 2018, 卷号: 304, 页码: 82-103 作者:  Yu, Hongshan;  Yang, Zhengeng;  Tan, Lei;  Wang, Yaonan;  Sun, Wei   |  收藏  |  浏览/下载：22/0  |  提交时间：2021/02/02 Semantic segmentation  Convolutional neural network  Markov random fields  Weakly supervised method  3D point clouds labeling   Methods and datasets on semantic segmentation: A review 期刊论文  OAI收割 NEUROCOMPUTING, 2018, 卷号: 304, 页码: 82-103 作者:  Yu HS(余洪山);  Yang, Zhengeng;  Tan, Lei;  Wang YN(王耀南);  Sun, Wei   |  收藏  |  浏览/下载：67/0  |  提交时间：2018/06/17 Semantic segmentation  Convolutional neural network  Markov random fields  Weakly supervised method  3D point clouds labeling   TextureGrow: Object recognition and segmentation with limit prior knowledge (EI CONFERENCE) 会议论文  OAI收割 2011 International Conference on Network Computing and Information Security, NCIS 2011, May 14, 2011 - May 15, 2011, Guilin, Guangxi, China Yao Z.; Han Q. 收藏  |  浏览/下载：24/0  |  提交时间：2013/03/25 In this paper we present a new method for automatically visual recognition and semantic segmentation of photographs. Our automatically and rapidly approach based on Cellular Automation. Most of the analysis and description of recognition and segmentation are based on statistical or structural properties of this attribute  most of them need plenty of samples and prior Knowledge. In this paper  within a few evident samples (not too many)  we can first get the texture feature of each component and the structures  then select the approximately location randomly of the objects or patches of them  then we use the Cellular Automata algorithm to "grow" based on texture of different objects. The grow progress will stop When texture grow to the boundary. By this steps a new method is found which allow us use very few samples and low lever experience and get a rapidly and accuracy way to recognize and segment objects. We found that this new propose gives competitive results with limited experience and samples. 2011 IEEE.