Abstract | ||
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Scene matching is used in the vision based automated navigation error correction technique in the absence of global positioning systems for unmanned aerial vehicles. When knowledge of landmarks in the scene is known a priori, the scene matching can be carried out in a more accurate and efficient way by considering a landmarks-only matching process. In this paper, we present two landmark selection algorithms where knowledge of landmarks in an aerial image is represented by color histograms which can be computed in advance. In landmark selection, one method treats the landmark selection as a population sampling problem and searches the population of a given landmark over the image via a Kullback Leibler type divergence measure. The other method computes the probability that an image point originates from a landmark and this probability is approximately calculated via the color histogram of the landmark. The performance of the two proposed algorithms is compared in a landmark detection scenario along with the selection results from a SUN saliency model trained using the landmark data as well. Experimental results show that the proposed algorithms are simple but effective for the landmark selection task. |
Year | Venue | Keywords |
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2014 | Fusion | landmark selection,landmarks-only matching process,vision based automated navigation error correction technique,image representation,landmark selection algorithms,color histogram,image matching,landmark detection scenario,population sampling,unmanned aerial vehicles,global positioning systems,computerised navigation,autonomous aerial vehicles,sun saliency model,scene matching,aerial image representation,path planning,color histogram knowledge,population sampling problem,uav,sampling methods,kullback leibler type divergence measure,likelihood,error correction,image colour analysis,probability,navigation,histograms,sociology |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 6 |
Name | Order | Citations | PageRank |
---|---|---|---|
Zhenlu Jin | 1 | 0 | 1.35 |
Xuezhi Wang | 2 | 94 | 10.88 |
Mark R. Morelande | 3 | 195 | 24.96 |
William Moran | 4 | 0 | 0.34 |
Quan Pan | 5 | 521 | 40.66 |
Chunhui Zhao | 6 | 97 | 26.94 |