Evaluating Visuospatial Features for Tracking Hazards in Overhead UAS Imagery-Reference-Cited by-同舟云学术

Evaluating Visuospatial Features for Tracking Hazards in Overhead UAS Imagery

Published:2021-10-12 Issue: Volume: Page:
ISSN:
Container-title:2021 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)
language:
Short-container-title:

Author:

Bajkowski Trevor M.¹,Hurt J. Alex¹,Dale Jeffrey¹,Huangal David¹,Keller James M.¹,Scott Grant J.¹,Price Stanton R.²

Affiliation:

1. University of Missouri-Columbia,Electrical Engineering and Computer Science Department,MO,Columbia

2. U.S. Army Engineer Research and Development Center,Vicksburg,MS

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9762007/9762067/09762206.pdf?arnumber=9762206

Reference16 articles.

1. Distinctive Image Features from Scale-Invariant Keypoints

2. ORB: An efficient alternative to SIFT or SURF

3. The estimation of the gradient of a density function, with applications in pattern recognition

4. A density-based al-gorithm for discovering clusters in large spatial databases with noise;ester;Proceedings of the Second International Conference on Knowledge Discovery and Data Mining KDD'96,0

5. A metric for distributions with applications to image databases

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Comparing visual co-registration methods for UAV and satellite RGB imagery with semantic filtering of key points;Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications VI;2024-06-07

2. Cohort Discovery from Bed Sensor Data with Fuzzy Evidence Accumulation Clustering;2023 IEEE International Conference on Fuzzy Systems (FUZZ);2023-08-13

3. Comparing hand-crafted and learned key-point feature extraction for co-location of sequential low-altitude UAS video frames;Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications V;2023-06-12