Timescape-Based Panoramic View for Historic Landmarks
Providing a panoramic view of famous landmarks around the world offers artistic and historic value for historians, tourists, and researchers. Exploring the history of famous landmarks by presenting a comprehensive view of a temporal panorama merged with geographical and historical information presents a unique challenge of dealing with images that span a long period, from the 1800’s up to the present. This work presents the concept of temporal panorama through a timeline display of aligned historic and modern images for many famous landmarks. Utilization of this panorama requires a collection of hundreds of thousands of landmark images from the Internet comprised of historic images and modern images of the digital age. These images have to be classified for subset selection to keep the more suitable images that chronologically document a landmark’s history. Processing of historic images captured using older analog technology under various different capturing conditions represents a big challenge when they have to be used with modern digital images. Successful processing of historic images to prepare them for next steps of temporal panorama creation represents an active contribution in cultural heritage preservation through the fulfillment of one of UNESCO goals in preservation and displaying famous worldwide landmarks.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.3461924Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 329
 Heider. K. Ali and Anthony Whitehead, “Registration of Modern and Historic Imagery for Timescape Creation,” Proceedings of 13th Conference for Computer and Robot Vision. Victoria, BC, Canada, 2016.
 Anthony D. Whitehead and James Opp, “Timescapes: Putting History in Your Hip Pocket” Proceedings of Computers and Their Applications Conference (CATA), Hawaii, USA, 2014.
 S. Agarwal, Y. Furukawaa, N. Snavely, I. Simonb, B. Curless, S. M. Seitz, and R. Szeliski,” Building Rome in a Day,” Communications of the ACM, Vol. 54, No. 10, 2011.
 P. Baudisch, D. Tan, D. Steedly, E. Rudolph, M. Uyttendaele, C. Pal, and R. Szeliski, “Panoramic Viewfinder: Providing a Real-time Preview to Help Users Avoid Flaws in Panoramic Pictures,” Proceedings of 17th Australian Conference for the Computer-Human Interaction Special Interest Group of the Human Factors Society of Australia, 2005.
 M. Brown and D. G. Lowe, “Recognising panoramas, “Proceedings of the 9th International Conference on Computer Vision (ICCV03), Vol. 2, 2003.
 R. J. Hyndman and A. B Koehler, “Another look at measures of forecast accuracy,” International Journal of Forecasting, Vol. 22, Issue 4, 2006.
 Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image Quality Assessment: From Error Visibility to Structural Similarity,” IEEE Transaction on Image Processing, Vol. 13, No. 4, 2004.
 S. Leutenegger, M. Chli and R. Y. Siegwart, “BRISK: Binary Robust Invariant Scalable Keypoints,” Proceedings of IEEE Int Conference on Computer Vision, 2011.
 S. Agarwal, Y. Furukawa, N. Snavely, B. Curless, S. S. Seitz,and R. Szeliski, “Reconstructing Rome,” IEEE Computer Journal, Vol. 43,Iss. 6, 2010.
 R. Martin-Brualla, D. Gallup, and S. M. Seitz, “Time-lapse Mining from Internet Photos,” ACM Transaction on Graphics, Vol. 34, No. 4, Article 6, 2015.
 H. K. Ali and A. Whitehead, “Image Subset Selection Using Gabor Filters and Neural Networks,” International Journal of Multimedia & Its Applications (IJMA) Vol.7, No.2, 2015.
 M. Nixon and A. Aguado. Feature Extraction & Image Processing for Computer Vision. 3rd Ed Academic Press, 2012.
 S. Chang, W.Chen, and H. Sundaram, “Semantic Visual Templates: Linking Visual Features to Semantics,” Proceedings of International Conference of Image Processing, 1998.
 N. A. Mahmon and N. Ya’acob, “A Review on Classification of Satellite Image Using Artificial Neural Network (ANN),” IEEE 5th Control and System Graduate Research Colloquium, 2014.
 J. Ma, J. C. Chan, and F. Canters, “Fully Automatic Subpixel Image Registration of Multiangle CHRIS/Proba Data,” IEEE Transaction on Geoscience and Remote Sensing, Vol. 44, No. 7, 2010.
 O. Arandjelović, D. Pham, and S.Venkatesh, “Efficient and accurate set-based registration of time-separated aerial images,” Journal of Pattern Recognition, Vol. 48, Issue 11, 2015.
 Y. Boykov, O. Veksler, and R. Zabih, “Fast Approximate Energy Minimization via Graph Cuts,” Proceedings of 7th IEEE International Conference on Computer Vision, 1999.
 Mark J. Hannah, “Computer Matching of Areas in Stereo Images,” PhD thesis, Stanford University, 1974.
 K. J. Borowiecki, N. Forbes, and A. Fresa, “Cultural Heritage in a Changing World,” Springer International Publishing, 2016.
 G. T. Tanselle. Literature and Artifacts. 1st Edition., Oak Knoll Press, 1998.
 UNESCO. New World Heritage Sites in Danger. In 34th Session of the UNESCO Committee, 2010.
 Dennis Gabor, “Theory of communications,” Journal of Int Electrical Engineers, Vol. 93, 1943.
 J. Kamarainen, V. Kyrki and H. Kalvinen, “Fundamental frequency Gabor filters for object recognition,” Proceedings of the 16th International Conference on Pattern Recognition, 2002.
 Z. Zexu and C. Pingyuan, “A Reliable Method of Image Registration Based on Optical Flow Field and Feature Extraction,” Chinese Journal of Electronics, Vol. 17, No. 1, 2008.
 D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” International Journal of Computer Vision. Vol. 60, Issue 2, 2004.
 B. Glocker, N. Komodakis, G. Tziritas, N. Navab, and N. Paragios, (2008) “Dense Image Registration through MRFs and Efficient Linear Programming” Medical Image Analysis, Vol. 12, No. 6.
 P. F. Felzenszwalb and D. P. Huttenlocher, “Efficient Belief Propagation for Early Vision,” Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2006.
 M. Gong, S. Zhao, L. Jiao, D. Tian, and S. Wan, “A Novel Coarse-to-Fine Scheme for Automatic Image Registration Based on SIFT and Mutual Information” IEEE Transaction on Geoscience and Remote Sensing, Vol. 52, No. 7, 2014.
 H. Bay, T. Tuytelaars, and L. V. Gool, “Surf: Speeded up robust features,” Proceedings of European Conference on Computer Vision, 2008.