Paper Info
Title
Hierarchical RANSAC for accurate horizon detection
Abstract
The horizon in marine scenes provides an important prior feature for unmanned surface vehicles (USV) based research and applications. However, most of existing research in horizon detection usually consider specific or simple scenarios. In this paper, we propose a novel approach to detect the horizon in maritime images with various situations by applying the algorithm of random sample consensus (RANSAC) hierarchically. First, a rough horizon line is estimated with RANSAC in the gradient map of downsized image. Thus, a region of interest (ROI) is defined by the neighborhood of the estimated horizon. Then a proper amount of patches are sampled from the edge map of the original image in the ROI, and a straight line is fitted in each patch using RANSAC. Finally, patches with lower rate of outliers for their fitted lines are selected and aggregated to compute the final horizon via RANSAC. Experimental results on our own dataset with diverse scenarios demonstrate that the proposed approach is more robust and more accurate than the traditional methods.
Year
DOI
Venue
2016
10.1109/MED.2016.7535933
2016 24th Mediterranean Conference on Control and Automation (MED)
Keywords
Field
DocType
hierarchical RANSAC,accurate horizon detection,marine scenes,unmanned surface vehicles,USV,random sample consensus,gradient map,downsized image,region of interest,ROI
Line (geometry),Computer vision,Computer science,RANSAC,Horizon,Outlier,Artificial intelligence,Sampling (statistics),Region of interest
Conference
ISSN
ISBN
Citations 
2325-369X
978-1-4673-8347-9
0
PageRank 
References 
Authors
0.34
7
3
Name
Order
Citations
PageRank
Xiaozheng Mou192.35
Bok-Suk Shin2689.27
Han Wang314822.31