Super Long Interval Time-Lapse Image Generation for Proactive Preservation of Cultural Heritage Using Crowdsourcing

To establish advanced analytical methods for preserving cultural heritage, this research proposes a method to generate a time-lapse image with a super-long temporal interval. The key issue is to realize an image collection method using crowdsourcing and a method to improve the matching accuracy between images of cultural heritage buildings captured 50 to 100 years ago and current images. As degradation and damage to the appearance of cultural heritage buildings occurs due to ageing, rebuilding, and renovation, image features of the timed images are changed. This decreases the accuracy of the matching process that uses the appearance of patch-region. In addition, we need to give more consideration to incorrect feature correspondence that is prominent in buildings with considerable symmetry. We aim to solve these difficulties by applying an Autoencoder and a guided matching method. Our method involves utilizing the function of crowdsourcing, which can easily obtain the current image captured at the same position and orientation as the past image. We propose this method to address the inability to obtain the correspondence points between two images when observation times are significantly different.

[1]  Steven M. Seitz,et al.  3D Time-Lapse Reconstruction from Internet Photos , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[2]  Noah Snavely,et al.  Image matching using local symmetry features , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[3]  Yoichi Sato,et al.  Shape-Preserving Half-Projective Warps for Image Stitching , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[4]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[5]  Maria Filomena Macedo,et al.  Biological colonization and biodeterioration of architectural ceramic materials: An overview , 2015 .

[6]  Tom Drummond,et al.  Faster and Better: A Machine Learning Approach to Corner Detection , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Giulia Caneva,et al.  Biological colonization patterns on the ruins of Angkor temples (Cambodia) in the biodeterioration vs bioprotection debate , 2014 .

[8]  Torsten Sattler,et al.  Merging the Unmatchable: Stitching Visually Disconnected SfM Models , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[9]  Zoltan-Csaba Marton,et al.  Implicit 3D Orientation Learning for 6D Object Detection from RGB Images , 2018, ECCV.

[10]  Markus Vincze,et al.  Guided Matching Based on Statistical Optical Flow for Fast and Robust Correspondence Analysis , 2016, ECCV.

[11]  Steven M. Seitz,et al.  Time-lapse mining from internet photos , 2015, ACM Trans. Graph..

[12]  Wei Jiang,et al.  Video stitching with spatial-temporal content-preserving warping , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[13]  Youhei Kawamura,et al.  Time-Lapse Image Generation using Image-Based Modeling by Crowdsourcing , 2018, 2018 IEEE International Conference on Big Data (Big Data).

[14]  Matthew A. Brown,et al.  Automatic Panoramic Image Stitching using Invariant Features , 2007, International Journal of Computer Vision.

[15]  Sharath Pankanti,et al.  Adaptive as-natural-as-possible image stitching , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[16]  Christopher Hunt,et al.  Notes on the OpenSURF Library , 2009 .