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  • Hindawi  (2)
  • 1
    Publikationsdatum: 2018-01-01
    Beschreibung: Based on the precipitation  δ18O values from the datasets of the Global Network of Isotopes in Precipitation (GNIP), the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) Reanalysis data, and previous researches, we explored the temporal and spatial variations of precipitation  δ18O in a typical monsoon climate zone, the Pearl River basin (PRB), and adjacent regions. The results showed that the temporal variations of precipitation  δ18O for stations should be correlated with water vapor sources, the distance of water vapor transport, the changes in location, and intensity of the intertropical convergence zone (ITCZ) rather than “amount effect.” Meanwhile, local meteorological and geographical factors showed close correlations with mean weighted precipitation  δ18O values, suggesting that “altitude effect” and local meteorological conditions were significant for the spatial variations of precipitation  δ18O. Moreover, we established linear regression models for estimating the mean weighted precipitation  δ18O values, which could better estimate variations in precipitation  δ18O than the Bowen and Wilkinson model in the PRB and adjacent regions.
    Print ISSN: 1687-9309
    Digitale ISSN: 1687-9317
    Thema: Geologie und Paläontologie , Physik
    Publiziert von Hindawi
    Standort Signatur Erwartet Verfügbarkeit
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  • 2
    Publikationsdatum: 2015-01-01
    Beschreibung: Recent years have seen greater interests in the tracking-by-detection methods in the visual object tracking, because of their excellent tracking performance. But most existing methods fix the scale which makes the trackers unreliable to handle large scale variations in complex scenes. In this paper, we decompose the tracking into target translation and scale prediction. We adopt a scale estimation approach based on the tracking-by-detection framework, develop a new model update scheme, and present a robust correlation tracking algorithm with discriminative correlation filters. The approach works by learning the translation and scale correlation filters. We obtain the target translation and scale by finding the maximum output response of the learned correlation filters and then online update the target models. Extensive experiments results on 12 challenging benchmark sequences show that the proposed tracking approach reduces the average center location error (CLE) by 6.8 pixels, significantly improves the performance by 17.5% in the average success rate (SR) and by 5.4% in the average distance precision (DP) compared to the second best one of the other five excellent existing tracking algorithms, and is robust to appearance variations introduced by scale variations, pose variations, illumination changes, partial occlusion, fast motion, rotation, and background clutter.
    Print ISSN: 1024-123X
    Digitale ISSN: 1563-5147
    Thema: Mathematik , Technik allgemein
    Publiziert von Hindawi
    Standort Signatur Erwartet Verfügbarkeit
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