Paper Info
Title
Topology Graph Pruning for Optical Mapping Methods using Edge Betweenness Centrality
Abstract
Optical mapping is one of the most widely used application areas of low-cost robotic platforms. These platforms are in favor as they are relatively easy to use, to operate and to maintain. Acquired optical data (in the form of video and/or image) are valuable sources of information for both online (e.g., navigation, localization, mapping, and others) and offline processes (scientific interpretations, change detection, mapping, and others). The amount of data acquired has been continuously growing thanks to the emerging capabilities of mobile platforms in terms of autonomy allowing longer surveying time. This increases the need for fast and efficient methods to process the obtained data. Creating optical 2D maps from acquired data is composed of mainly image matching, trajectory estimation (Global Alignment (GA)) and image blending steps. In this paper, we discuss the usage of Edge Betweenness Centrality (EBC) concept to reduce the total number of overlapping image pairs to be used in the GA step. EBC allows selecting the image pairs that play a relatively key role in the topology graph. We also discuss the usage of graph energy as a decision criterion during image mosaicing iterations. We present experiments with several datasets to show the performance of the proposed method.
Year
DOI
Venue
2019
10.1109/ICARM.2019.8833633
2019 IEEE 4th International Conference on Advanced Robotics and Mechatronics (ICARM)
Keywords
Field
DocType
image matching,image blending steps,overlapping image pairs,graph energy,image mosaicing iterations,topology graph pruning,optical mapping methods,edge betweenness centrality,optical 2D maps,decision criterion,trajectory estimation,global alignment
Graph,Topology,3D optical data storage,Graph energy,Change detection,Optical mapping,Computer science,Betweenness centrality,Trajectory,Pruning
Conference
ISBN
Citations 
PageRank 
978-1-7281-0065-4
0
0.34
References 
Authors
8
2
Name
Order
Citations
PageRank
Armagan Elibol102.37
Nak Young Chong240356.29