中国科学院机构知识库网格系统: 检索

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	IGE-LIO: Intensity Gradient Enhanced Tightly Coupled LiDAR-Inertial Odometry 期刊论文 OAI收割 IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2024, 卷号: 73 作者: Chen, Ziyu; Zhu, Hui; Yu, Biao; Jiang, Chunmao; Hua, Chen \| 收藏 \| 浏览/下载：8/0 \| 提交时间：2024/11/20 Laser radar Feature extraction Simultaneous localization and mapping Noise Accuracy Location awareness Data mining Degenerated environments intensity gradient localization simultaneous localization and mapping (SLAM) weighting function
	Intensity/Inertial Integration-Aided Feature Tracking on Event Cameras 期刊论文 OAI收割 REMOTE SENSING, 2022, 卷号: 14, 期号: 8, 页码: 15 作者: Li, Zeyu; Liu, Yong; Zhou, Feng; Li, Xiaowan \| 收藏 \| 浏览/下载：29/0 \| 提交时间：2022/08/15 event camera feature tracking intensity inertial integration
	Features Combined Binary Descriptor Based on Voted Ring-Sampling Pattern 期刊论文 OAI收割 IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2020, 卷号: 30, 期号: 10, 页码: 3675-3687 作者: Liu, Hongmin; Zhang, Qianqian; Fan, Bin; Wang, Zhiheng; Han, Junwei \| 收藏 \| 浏览/下载：26/0 \| 提交时间：2021/01/07 Histograms Feature extraction Encoding Task analysis Fans Computational efficiency Memory management Binary descriptor combined features gradient feature intensity feature ring-sampling pattern voting strategy
	Physiological Signal-Based Method for Measurement of Pain Intensity 期刊论文 OAI收割 FRONTIERS IN NEUROSCIENCE, 2017, 卷号: 11, 页码: 1-13 作者: Su, Yang; Han JD(韩建达); Zhao XG(赵新刚); Chu YQ(褚亚奇) \| 收藏 \| 浏览/下载：27/0 \| 提交时间：2017/08/20 feature extraction feature selection and reduction pain intensity quantification physiological signals pattern classification
	Exploring Local and Overall Ordinal Information for Robust Feature Description 期刊论文 OAI收割 IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 卷号: 38, 期号: 11, 页码: 2198-2211 作者: Zhenhua Wang; Bin Fan; Gang Wang; Fuchao Wu \| 收藏 \| 浏览/下载：40/0 \| 提交时间：2018/01/03 Feature Description Intensity Order Illumination Invariance Image Matching
	Improved bore-sight calibration for airborne light detection and ranging using planar patches 期刊论文 OAI收割 Journal of Applied Remote Sensing, 2016, 卷号: 10, 期号: 2 作者: Li, Dong; Guo, Huadong; Wang, Cheng; Dong, Pinliang; Zuo, Zhengli 收藏 \| 浏览/下载：28/0 \| 提交时间：2017/04/24 SPECIES CLASSIFICATION FEATURE PARAMETERS LIDAR INTENSITY CANOPY FOREST TERRESTRIAL
	A matching algorithm on statistical properties of Harris corner (EI CONFERENCE) 会议论文 OAI收割 2011 International Conference on Information and Automation, ICIA 2011, June 6, 2011 - June 8, 2011, Shenzhen, China 作者: He B. 收藏 \| 浏览/下载：21/0 \| 提交时间：2013/03/25 The fundamental goal of target recognition and video tracking is to match target template with source image. Most matching methods are based on image intensity or multi-feature points. And the latter method is more popular for its high accuracy and small calculation. Image Registration Based on Feature Points focus on effective feature extraction of image points and paradigm. Harris corner in the image rotation gray noise and viewpoint change conditions has an ideal match results is more recent application of one feature point. This paper extract the Harris corner deviation and covariance firstly experiments show that the two features exclusive then applied them to image registration for the first time. A set of actual images have shown this proposed method not only overcomes the complicated background gray uneven distribution problems but also pan and zoom the image has a good resistance. 2011 IEEE.
	A Partial Intensity Invariant Feature Descriptor for Multimodal Retinal Image Registration 期刊论文 OAI收割 IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, 2010, 卷号: 57, 期号: 7, 页码: 1707-1718 作者: Chen, Jian; Tian, Jie; Lee, Noah; Zheng, Jian; Smith, R. Theodore 收藏 \| 浏览/下载：19/0 \| 提交时间：2015/11/08 Harris detector local feature multimodal registration partial intensity invariance retinal images
	Integrated intensity, orientation code and spatial information for robust tracking (EI CONFERENCE) 会议论文 OAI收割 2007 2nd IEEE Conference on Industrial Electronics and Applications, ICIEA 2007, May 23, 2007 - May 25, 2007, Harbin, China 作者: Wang Y.; Wang Y.; Wang Y.; Wang Y.; Wang Y. 收藏 \| 浏览/下载：28/0 \| 提交时间：2013/03/25 real-time tracking is an important topic in computer vision. Conventional single cue algorithms typically fail outside limited tracking conditions. Integration of multimodal visual cues with complementary failure modes allows tracking to continue despite losing individual cues. In this paper we combine intensity orientation codes and special information to form a new intensity-orientation codes-special (IOS) feature to represent the target. The intensity feature is not affected by the shape variance of object and has good stability. Orientation codes matching is robust for searching object in cluttered environments even in the cases of illumination fluctuations resulting from shadowing or highlighting etc The spatial locations of the pixels are used which allow us to take into account the spatial information which is lost in traditional histogram. Histograms of intensity orientation codes and spatial information are employed for represent the target Mean shift algorithm is a nonparametric density estimation method. The fast and optimal mode matching can be achieved by this method. In order to reduce the compute time we use the mean shift procedure to reach the target localization. Experiment results show that the new method can successfully cope with clutter partial occlusions illumination change and target variations such as scale and rotation. The computational complexity is very low. If the size of the target is 3628 pixels it only needs 12ms to complete the method. 2007 IEEE.