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	Multi-Source Image Matching Algorithms for UAV Positioning: Benchmarking, Innovation, and Combined Strategies 期刊论文 OAI收割 REMOTE SENSING, 2024, 卷号: 16, 期号: 16, 页码: 18 作者: Liu, Jianli; Xiao, Jincheng; Ren, Yafeng; Liu, Fei; Yue, Huanyin \| 收藏 \| 浏览/下载：7/0 \| 提交时间：2024/11/28 UAV positioning image matching features keypoints benchmarking consistency verification combined strategies
	Monocular 3d object detection based on uncertainty prediction of keypoints 期刊论文 OAI收割 Machines, 2022, 卷号: 10, 期号: 1, 页码: 1-16 作者: Chen M(陈牧); Zhao HC(赵怀慈); Liu PF(刘鹏飞) \| 收藏 \| 浏览/下载：39/0 \| 提交时间：2022/01/13 Keypoints Monocular 3D detection Uncertainty prediction
	Real-time SIFT-based Object Recognition System 会议论文 OAI收割 Takamatsu, Japan, Aug, 4-7, 2013 作者: Wang, Zhao; Xiao, Han; He, Wenhao; Wen, Feng; Yuan, Kui \| 收藏 \| 浏览/下载：28/0 \| 提交时间：2016/10/17 Object Recognition Sift Keypoints Embedded System K-d Tree Bbf Algorithm
	Discriminative Object Tracking via Sparse Representation and Online Dictionary Learning 期刊论文 OAI收割 IEEE Transactions on Cybernetics, 2013, 期号: 44, 页码: 539-553 作者: Xie, Yuan; Zhang, Wensheng; Li, Cuihua; Lin, Shuyang; Qu, Yanyun \| 收藏 \| 浏览/下载：12/0 \| 提交时间：2020/05/26 Dictionary Learning Object Tracking Robust Keypoints Matching Sparse Representation.
	Features extraction and matching of teeth image based on the SIFT algorithm (EI CONFERENCE) 会议论文 OAI收割 2012 2nd International Conference on Computer Application and System Modeling, ICCASM 2012, July 27, 2012 - July 29, 2012, Shenyang, China 作者: Wang X.; Wang X.; Wang X. 收藏 \| 浏览/下载：28/0 \| 提交时间：2013/03/25 Using of SIFT algorithm in the image of teeth model can detect the features of the teeth image effectively. In this approach first search over all scales and image locations by using a difference-of-Gaussian function to identify potential interest points that are invariant to scale and orientation. Second select keypoints based on measures of their stability and a detailed model is fit to determine location and scale at each candidate location. Third assign one or more orientations to each keypoint location based on local image gradient directions. Last measure the local image gradients at the selected scale in the region around each keypoint. And then use the KNN algorithm to match the features. Through lots of experiments and comparing with other feature extraction methods this method can detect the features of the teeth model effectively and offer some available parameters for 3D reconstruction of the teeth model. the authors.
	视频运动分析与事件识别学位论文 OAI收割工学博士, 中国科学院自动化研究所: 中国科学院研究生院, 2010 作者: 李莉收藏 \| 浏览/下载：120/0 \| 提交时间：2015/09/02 视频结构分析镜头检测关键帧提取视频事件识别镜头分类特征融合主导集聚类复发注意机制兴趣点时空兴趣点 video structure analysis shot detection key frame extraction video event recognition shot classification feature fusion dominant set clustering algorithm attention machanism keypoints spatio-temporal interest points