机构

• 自动化研究所 [3]
• 合肥物质科学研究院 [2]
• 长春光学精密机械与物... [1]
• 上海神经科学研究所 [1]
• 遥感与数字地球研究所 [1]
• 水生生物研究所 [1]
• 更多

采集方式

• OAI收割 [12]

内容类型

• 期刊论文 [10]
• 会议论文 [2]

发表日期

• 2024 [1]
• 2022 [3]
• 2021 [2]
• 2020 [2]
• 2019 [1]
• 2008 [1]
• 更多

学科主题

• Neuroscien... [1]
• Plant Scie... [1]

筛选

浏览/检索结果: 共12条，第1-10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 Rethinking Global Context in Crowd Counting 期刊论文  OAI收割 Machine Intelligence Research, 2024, 卷号: 21, 期号: 4, 页码: 640-651 作者:  Guolei Sun;   Yun Liu;   Thomas Probst;   Danda Pani Paudel;  Nikola Popovic   |  收藏  |  浏览/下载：7/0  |  提交时间：2024/07/18 Crowd counting  vision transformer  global context  attention  density map   SSR-HEF: Crowd Counting With Multiscale Semantic Refining and Hard Example Focusing 期刊论文  OAI收割 IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2022, 卷号: 18 作者:  Chen, Jiwei;  Wang, Kewei;  Su, Wen;  Wang, Zengfu   |  收藏  |  浏览/下载：47/0  |  提交时间：2022/12/23 Semantics  Task analysis  Feature extraction  Focusing  Informatics  Estimation  Prediction algorithms  Crowd counting  density map  hard example focusing (HEF)  multiscale semantic refining strategy (SSR)   A multi-branch convolutional neural network with density map for aphid counting 期刊论文  OAI收割 BIOSYSTEMS ENGINEERING, 2022, 卷号: 213 作者:  Li, Rui;  Wang, Rujing   |  收藏  |  浏览/下载：21/0  |  提交时间：2022/12/23 Density map  Aphid counting  Convolutional neural network  Deep learning   Complex and reticulate origin of edible roses (Rosa, Rosaceae) in China 期刊论文  OAI收割 HORTICULTURE RESEARCH, 2022, 卷号: 9, 页码: uhab051 作者:  Cui, Wei-Hua;  Du, Xin-Yu;  Zhong, Mi-Cai;  Fang, Wei;  Suo, Zhi-Quan   |  收藏  |  浏览/下载：6/0  |  提交时间：2024/04/30 NUCLEAR-DNA CONTENT  COMPLETE CHLOROPLAST GENOME  DENSITY GENETIC-MAP  GENUS ROSA  PHYLOGENETIC-RELATIONSHIPS  MOLECULAR EVIDENCE  RIBOSOMAL DNA  GARDEN ROSES  WILD ROSES  EVOLUTION   Density-Aware Multi-Task Learning for Crowd Counting 期刊论文  OAI收割 IEEE TRANSACTIONS ON MULTIMEDIA, 2021, 卷号: 23, 页码: 443-453 作者:  Jiang, Xiaoheng;  Zhang, Li;  Zhang, Tianzhu;  Lv, Pei;  Zhou, Bing   |  收藏  |  浏览/下载：56/0  |  提交时间：2021/03/08 Task analysis  Semantics  Estimation  Feature extraction  Convolutional neural networks  Cameras  Head  Convolutional neural network  crowd counting  density-level classification  density map estimation  multi-task learning   Tracking-by-Counting: Using Network Flows on Crowd Density Maps for Tracking Multiple Targets 期刊论文  OAI收割 IEEE Transactions on Image Processing, 2021, 卷号: 30, 页码: 1439-1452 作者:  Ren WH(任卫红);  Wang, Xinchao;  Tian JD(田建东);  Tang YD(唐延东);  Chan, Antoni B.   |  收藏  |  浏览/下载：63/0  |  提交时间：2021/01/17 People tracking  crowd density map  multiple people tracking  flow tracking   Construction of the first high-density genetic map for growth related QTL analysis in Ancherythroculter nigrocauda 期刊论文  OAI收割 JOURNAL OF OCEANOLOGY AND LIMNOLOGY, 2020, 页码: 13 作者:  Sun Yanhong;  Li Pei;  Wang Guiying;  Sun Renli;  Chen Jian   |  收藏  |  浏览/下载：48/0  |  提交时间：2021/01/15 Ancherythroculter nigrocauda  specific-locus amplified fragment  high-density genetic map  quantitative trait locus   Cross-Level Parallel Network for Crowd Counting 期刊论文  OAI收割 IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2020, 卷号: 16, 期号: 1, 页码: 566-576 作者:  Li, Jing;  Xue, Yaokai;  Wang, Weiqun;  Ouyang, Gaoxiang   |  收藏  |  浏览/下载：74/0  |  提交时间：2020/03/30 Convolutional neural network (CNN)  cross-level and multiscale features  crowd counting  density map  scale aggregation network   Unraveling the genetic architecture of grain size in einkorn wheat through linkage and homology mapping and transcriptomic profiling 期刊论文  OAI收割 JOURNAL OF EXPERIMENTAL BOTANY, 2019, 卷号: 70, 期号: 18, 页码: 4671-4687 作者:  Yu, Kang;  Liu, Dongcheng;  Chen, Yong;  Wang, Dongzhi;  Yang, Wenlong   |  收藏  |  浏览/下载：41/0  |  提交时间：2022/01/06 Einkorn wheat (Triticum monococcum)  grain size  high-density genetic map  quantitative trait loci  RAD-seq  RNA-seq   Design of a scene simulator in land-based aerospace lab with the man-made light source system based on quantum theory (EI CONFERENCE) 会议论文  OAI收割 Optoelectronic Devices and Integration II, November 12, 2007 - November 15, 2007, Beijing, China 作者:  Fang W.;  Yi W. 收藏  |  浏览/下载：29/0  |  提交时间：2013/03/25 The paper presents a method with the technique of combination filtering of spectrum and the technique of DSP control for the simulation and recognition of the spontaneously lightening object and stellar map in the universe. The magnitude and the spectrum of a star can be simulated in a wide dynamic range. We established the mathematical models of the Vega's visible light spectrum and luminous flux density. Tungsten-halogen lamp as a light source is used to fit the linear light of the spontaneously lightening object in the universe. Through the experiment  it shows that the more sub-channels there are while filtering spectrum in the visible spectrum  and the more degrees the variable neutral density filters and the narrow-band attenuators have  more close to the actual spectrum the simulated spectrum is. It is proved that using the PID technique is beneficial to the output of the accurate and steady radiation from the light sources. The experiment shows that the using of this method can get perfect result in fitting and simulating the spectrum  magnitude and the stellar map of the spontaneously lightening object . The analysis of the consistent result and the experimental error is also discussed in detail in this paper.