Title
Shape point set matching based on oriented shape context in turbulence-cluttered scene
Abstract
The main challenges of shape point set matching in a long-distance imaging scene stem from the optical turbulence effects, which lead to shape object deformation, rotation, shifted object positions, and cluttered outliers. To address this problem, we propose an effective energy cost model with figural continuity constraint. We first construct an Oriented Shape Context (OSC) descriptor using attributes of shape edges' length and direction, which represent rotation invariance, by adding the oriented model (prototype) edges point set. Then, inspired by the figural continuity prior between the model and target point set, we transform the continuity constraint into a matching energy cost model. Lastly, we develop a simple 2-tree graph to minimize the matching cost function using the Dynamic Program (DP) optimization algorithm. The extensive experiments on both synthetic and real data validate that the proposed method can effectively detect the desired shapes in the complex and highly turbulence-cluttered scenes.
Year
DOI
Venue
2020
10.1007/s11042-020-09215-8
MULTIMEDIA TOOLS AND APPLICATIONS
Keywords
DocType
Volume
Imaging through turbulent media,Shape invariant descriptor,Cluttered scene,Shape context,Contour shape recognition
Journal
79.0
Issue
ISSN
Citations 
35-36
1380-7501
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Xinggui Xu100.68
Ping Yang200.68
Hao Xian300.34
Yong Liu4145.44