References 1 Google Street View Image API 2 Garmin virb360 2018

References 1 google street view image api 2 garmin

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References 1. Google Street View Image API 2. Garmin virb360 (2018). 3. GoPro fusion (2018). 4. Insta360 evo (2018). 5. Vuze 3D 360 video camera from humaneyes (2018). 6. Alahi, A., Ortiz, R., Vandergheynst, P.: Freak: fast retina keypoint. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 510–517, June 2012
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588 M. Marcon et al. 7. Aqel, M., Marhaban, M.H., Saripan, M.I., Ismail, N.: Review of visual odometry: types, approaches, challenges, and applications. SpringerPlus 5 , 12 (2016) 8. Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24 (6), 381–395 (1981) 9. Gledhill, D., Tian, G.Y., Taylor, D., Clarke, D.: Panoramic imaging–a review. Comput. Graph. 27 , 435–445 (2003) 10. Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision, 2nd edn. Cambridge University Press, Cambridge (2004) 11. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Com- put. Vision 60 (2), 91–110 (2004) 12. Mei, C., Benhimane, S., Malis, E., Rives, P.: Efficient homography-based tracking and 3-D reconstruction for single-viewpoint sensors. IEEE Trans. Rob. 24 (6), 1352– 1364 (2008) 13. Mei, C., Rives, P.: Single view point omnidirectional camera calibration from pla- nar grids. In: Proceedings 2007 IEEE International Conference on Robotics and Automation, pp. 3945–3950, April 2007 14. Pretto, A., Menegatti, E., Pagello, E.: Omnidirectional dense large-scale mapping and navigation based on meaningful triangulation. In: 2011 IEEE International Conference on Robotics and Automation, pp. 3289–3296. IEEE, May 2011 15. Tardif, J.-P., Pavlidis, Y., Daniilidis, K.: Monocular visual odometry in urban environments using an omnidirectional camera. In: 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2531–2538. IEEE, September 2008 16. Torii, A., Havlena, M., Pajdla, T.: From Google street view to 3D city models. In: 2009 IEEE 12th International Conference on Computer Vision Workshops ICCV Workshops, pp. 2188–2195 (2009) 17. Torii, A., Imiya, A., Ohnishi, N.: Two- and three- view geometry for spherical cameras. In: OMNIVIS (2005) 18. Valiente, D., Gil, A., Reinoso, S., Juli´a, M., Holloway, M.: Improved omnidirectional odometry for a view-based mapping approach. Sensors 17 (2), 325 (2017)
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Drivers and Barriers for Open Government Data Adoption: An Isomorphic Neo-Institutional Perspective Henry N. Roa 1( & ) , Edison Loza-Aguirre 2 , and Pamela Flores 2 1 Facultad de Ingenier í a, Ponti fi cia Universidad Cat ó lica del Ecuador, Av. 12 de Octubre 1076 y Roca, P.O. Box 17-01-2184, Quito, Ecuador [email protected] 2 Facultad de Ingenier í a en Sistemas, Escuela Polit é cnica Nacional, Ladr ó n de Guevara, P.O. Box 17-01-2759, E11-253 Quito, Ecuador {edison.loza,pamela.flores}@epn.edu.ec Abstract. By making government data available to all, Open Government Data (OGD) initiatives promote transparency, accountability and value creation. However, these initiatives face several problems affecting their implementation throughout its adoption process. This study focuses on the forces driving or hindering the adoption of OGD in a developing country at its early stages. In
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