Abstract
In this paper we show that Saliency-based keypoint selection makes natural landmark detection and object recognition quite effective and efficient, thus enabling augmented reality techniques in a plethora of applications in smart city contexts. As a case study we address a tour of a museum where a modern smart device like a tablet or smartphone can be used to recognize paintings, retrieve their pose and graphically overlay useful information.
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Buoncompagni, S., Maio, D., Maltoni, D., Papi, S.: Saliency-based keypoint selection for fast object detection and matching. Pattern Recognition Letters 62, 32–40 (2015). Elsevier
Calonder, M., Lepetit, V., Strecha, C., Fua, P.: BRIEF: binary robust independent elementary features. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part IV. LNCS, vol. 6314, pp. 778–792. Springer, Heidelberg (2010)
Van De Sande, K.E., Gevers, T., Snoek, C.G.: Evaluating color descriptors for object and scene recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 32(9), 1582–1596 (2010)
Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM 24(6), 381–395 (1981)
Parikh, D., Jancke, G.: Localization and segmentation of a 2D high capacity color barcode. In: IEEE Workshop on Applications of Computer Vision, WACV 2008, pp. 1–6, January 7–9, 2008
Furht, B.: Handbook of augmented reality, vol. 71. Springer, New York (2011)
Miyashita, T., et al.: An augmented reality museum guide. In: Proceedings of the 7th IEEE/ACM International Symposium on Mixed and Augmented Reality. IEEE Computer Society (2008)
Damala, A., Marchal, I., Houlier, P.: Merging augmented reality based features in mobile multimedia museum guides. In: CIPA Conference on Anticipating the Future of the Cultural Past, 2007, October 1–6, 2007
Damala, A., et al.: Bridging the gap between the digital and the physical: design and evaluation of a mobile augmented reality guide for the museum visit. In: Proceedings of the 3rd International Conference on Digital Interactive Media in Entertainment and Arts. ACM (2008)
Caridi, A., Coccoli, M., Volpi, V.: Wolfsoniana smart museum. a pilot plant installation of the PALM-cities project. In: UMAP Workshops (2013)
Rosten, E., Porter, R., Drummond, T.: Faster and better: A machine learning approach to corner detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 32(1), 105–119 (2010)
http://www.smart-cities.eu/download/smart_cities_final_report.pdf
Carneiro, G., Jepson, A.D.: The quantitative characterization of the distinctiveness and robustness of local image descriptors. Image and Vision Computing 27(8), 1143–1156 (2009)
Hartmann, W., Havlena, M., Schindler, K.: Predicting matchability. In: Conference on Computer Vision and Pattern Recognition (2014)
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Buoncompagni, S., Maio, D., Maltoni, D., Papi, S. (2015). Saliency-Based Keypoint Reduction for Augmented-Reality Applications in Smart Cities. In: Murino, V., Puppo, E., Sona, D., Cristani, M., Sansone, C. (eds) New Trends in Image Analysis and Processing -- ICIAP 2015 Workshops. ICIAP 2015. Lecture Notes in Computer Science(), vol 9281. Springer, Cham. https://doi.org/10.1007/978-3-319-23222-5_26
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DOI: https://doi.org/10.1007/978-3-319-23222-5_26
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