Robust power line extraction from aerial image using object-based Gaussian-Markov random field with gravity property parameters

2022 ◽  
pp. 116634
Author(s):  
Le Zhao ◽  
Hongtai Yao ◽  
Meng Tian ◽  
Xianpei Wang
Keyword(s):  
Random Field ◽  
Markov Random Field ◽  
Power Line ◽  
Aerial Image ◽  
Line Extraction ◽  
Gaussian Markov Random Field ◽  
Markov Random ◽  
Object Based

scholarly journals Power Line Extraction From Aerial Images Using Object-Based Markov Random Field With Anisotropic Weighted Penalty

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 125333-125356 ◽  
Author(s):  
Le Zhao ◽  
Xianpei Wang ◽  
Hongtai Yao ◽  
Meng Tian ◽  
Zini Jian
Keyword(s):  
Random Field ◽  
Markov Random Field ◽  
Power Line ◽  
Aerial Images ◽  
Line Extraction ◽  
Markov Random ◽  
Object Based

scholarly journals Mobile Robotic Sensors for Environmental Monitoring using Gaussian Markov Random Field

Robotica ◽  
2020 ◽  
pp. 1-23
Author(s):  
Linh Nguyen ◽  
Sarath Kodagoda ◽  
Ravindra Ranasinghe ◽  
Gamini Dissanayake
Keyword(s):  
Random Field ◽  
Markov Random Field ◽  
Adaptive Sampling ◽  
Published Data ◽  
Data Sets ◽  
Independence Property ◽  
Gaussian Markov Random Field ◽  
Markov Random ◽  
Sampling Optimization ◽  
Robotic Sensors

SUMMARY This paper addresses the issue of monitoring spatial environmental phenomena of interest utilizing information collected by a network of mobile, wireless, and noisy sensors that can take discrete measurements as they navigate through the environment. It is proposed to employ Gaussian Markov random field (GMRF) represented on an irregular discrete lattice by using the stochastic partial differential equations method to model the physical spatial field. It then derives a GMRF-based approach to effectively predict the field at unmeasured locations, given available observations, in both centralized and distributed manners. Furthermore, a novel but efficient optimality criterion is then proposed to design centralized and distributed adaptive sampling strategies for the mobile robotic sensors to find the most informative sampling paths in taking future measurements. By taking advantage of conditional independence property in the GMRF, the adaptive sampling optimization problem is proven to be resolved in a deterministic time. The effectiveness of the proposed approach is compared and demonstrated using pre-published data sets with appealing results.

Sign in / Sign up

Export Citation Format

Share Document