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Contemporary Digital Technologies at the Service of Cultural Heritage Read chapter Read first chapter

2018 | OriginalPaper | Chapter

An Image Dataset of Bishnupur Terracotta Temples for Digital Heritage Research

Authors : Mrinmoy Ghorai, Sanchayan Santra, Soumitra Samanta, Pulak Purkait, Bhabatosh Chanda

Published in: Heritage Preservation

Publisher: Springer Singapore

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Abstract

Heritage preservation and awareness building has become a major application domain for computer vision techniques in recent days. Bishnupur is an important and well-known heritage site in West Bengal, India. This attractive tourist place is famous for its terracotta temples. In this article, we present an image dataset created by us for developing and evaluating various computer vision algorithms for preservation and visualization of heritage artifacts in digital space. The dataset includes images of some important temples, such as Jor Bangla temple, Kalachand temple, Madan Mohan temple, Nandalal temple, Radha Madhav temple, Rasmancha, and Shyamrai temple. Though this dataset can be used for many types of computer vision and image analysis algorithms, we have shown here its usefulness by testing the images for four different applications: 3D reconstruction, image inpainting, texture classification, and content-specific figure spotting and retrieval. Note that we have shown the results of baseline methods only. The dataset is publicly available at http://​www.​isical.​ac.​in/​~bsnpr/​ for research purpose only.

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previous chapter Characterization, Detection, and Synchronization of Audio-Video Events in Bharatanatyam Adavus
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Footnotes
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The image is copied from http://​ccwu.​me/​vsfm/​.
 
Literature
1.
2.
3.
4.
5.
Barnes, C., Shechtman, E., Finkelstein, A., Goldman, D.B.: Patch-match: a randomized correspondence algorithm for structural image editing. ACM Trans. Graph. 28(3), 1–11 (2009)CrossRef
6.
Caputo, B., Hayman, E., Mallikarjuna, P.: Class-specific material categorisation. In: ICCV (2005)
7.
Chatterjee, A., Madhav Govindu, V.: Efficient and robust large-scale rotation averaging. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 521–528 (2013)
8.
Cohen, A., Zach, C., Sinha, S.N., Pollefeys, M.: Discovering and exploiting 3D symmetries in structure from motion. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1514–1521. IEEE (2012)
9.
Costa, A.F., Mamani, G.H., Traina, A.J.M.: An efficient algorithm for fractal analysis of textures. In: SIBGRAPI (2012)
10.
Criminisi, A., Perez, P., Toyama, K.: Region filling and object removal by exemplar-based inpainting. IEEE TIP 13(9) (2004)
11.
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR (2005)
12.
Darabi, S., Shechtman, E., Barnes, C., Goldman, D.B., Sen, P.: Image melding: combining inconsistent images using patch-based synthesis. ACM Trans. Graph. (TOG), 31(4), 82:1–82:10 (2012)CrossRef
13.
Efros, A., Leung, T.: Texture synthesis by non-parametric sampling. In: ICCV, vol. 2, pp. 1033–1038 (1999)
14.
Furukawa, Y., Ponce, J.: Accurate, dense, and robust multi-view stereopsis. IEEE TPAMI 32(8), 1362–1376 (2010)CrossRef
15.
Ghorai, M., Mandal, S., Chanda, B.: Image completion assisted by transformation domain patch approximation. In: Proceedings of the 2014 Indian Conference on Computer Vision Graphics and Image Processing, p. 66. ACM (2014)
16.
Giakoumis, I., Nikolaidis, N., Pitas, I.: Digital image processing techniques for the detection and removal of cracks in digitized paintings. IEEE Trans. Image Process. 15(1), 178–188 (2006)CrossRef
17.
Giakoumis, I., Pitas, I.: Digital restoration of painting cracks. In: Proceedings of the 1998 IEEE International Symposium on Circuits and Systems, 1998. ISCAS’98, vol. 4, pp. 269–272. IEEE (1998)
18.
Halder, S., Halder, M.: Temple Architecture of Bengal: Analysis of Stylistic Evolution from Fifth to Nineteenth Century. Urbee Prakashan, Kolkata (2011)
19.
Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural feature for image classification. IEEE TSMC 3(6), 610–621 (1973)
20.
Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision. Cambridge university press (2003)
21.
Hayman, E., Caputo, B., Fritz, M., Eklundh, J.-O.: On the significance of real-world conditions for material classification. In: ECCV (2004)
22.
Heinly, J., Schonberger, J.L., Dunn, E., Frahm, J.-M.: Reconstructing the world* in six days*(as captured by the Yahoo 100 million image dataset). In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3287–3295 (2015)
23.
Kim, T.K., Wong, S.F., Cipolla, R.: Tensor canonical correlation analysis for action classification. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8, June 2007
24.
Komodakis, N., Tziritas, G.: Image completion using efficient belief propagation via priority scheduling and dynamic pruning. IEEE TIP 16(11) (2007)MathSciNetCrossRef
25.
Krizhevsky, A., Sutskever, I., Hinton, G.: Imagenet classification with deep convolutional neural networks. In: NIPS (2012)
26.
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
27.
Levoy, M., Pulli, K., Curless, B., Rusinkiewicz, S., Koller, D., Pereira, L., Ginzton, M., Anderson, S., Davis, J., Ginsberg, J., et al.: The digital Michelangelo project: 3D scanning of large statues. In: CGIT, pp. 131–144 (2000)
28.
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. IJCV 60(2), 91–110 (2004)CrossRef
29.
Maji, S., Cimpoi, M., Kokkinos, I., Mohamed, S., Vedaldi, A.: Describing textures in the wild. In: CVPR (2014)
30.
Oliva, A., Torralba, A.: Modeling the shape of the scene: a holistic representation of the spatial envelope. Int. J. Comput. Vis. (IJCV) 42(3), 145–175 (2001)CrossRef
31.
Perronnin, F., Sanchez, J., Mensink, T.: Improving the fisher kernel for large-scale image classification. In: ECCV (2010)CrossRef
32.
Purkait, P., Chanda, B.: Digital restoration of damaged mural images. In: ICVGIP (2012)
33.
Remondino, F.: Heritage recording and 3D modeling with photogrammetry and 3D scanning. Remote Sens. 3(6), 1104–1138 (2011)CrossRef
34.
Ruzic, T., Pizurica, A.: Context-aware patch-based image inpainting using Markov random field modeling. IEEE Trans. Image Process. 24(1), 444–456 (2015)MathSciNetCrossRef
35.
Sahay, P., Rajagopalan, A.: Geometric inpainting of 3D structures. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 1–7 (2015)
36.
Seo, H.J., Milanfar, P.: Training-free, generic object detection using locally adaptive regression kernels. IEEE Trans. PAMI 32(9), 1688–1704 (2010)CrossRef
37.
Sharan, L., Liu, C., Rosenholtz, R., Adelson, E.H.: Recognizing materials using perceptually inspired features. IJCV 103(3), 348–371 (2013)MathSciNetCrossRef
38.
Shechtman, E., Irani, M.: Matching local self-similarities across images and videos. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8, June 2007
39.
40.
Snavely, N., Seitz, S.M., Szeliski, R.: Photo tourism: exploring photo collections in 3D. In: ACM TOG, vol. 25, pp. 835–846. ACM (2006)CrossRef
41.
Snavely, N., Seitz, S.M., Szeliski, R.: Modeling the world from internet photo collections. IJCV 80(2), 189–210 (2008)CrossRef
42.
Stanco, F., Tenze, L., Ramponi, G.: Virtual restoration of vintage photographic prints affected by foxing and water blotches. J. Electron. Imaging 14(4), 043008–043008 (2005)CrossRef
43.
Stanco, F., Tenze, L., Ramponi, G.: Technique to correct yellowing and foxing in antique books. IET Image Process. 1(2), 123–133 (2007)CrossRef
44.
Wexler, Y., Shechtman, E., Irani, M.: Space-time completion of video. IEEE TPAMI 29(3), 463–476 (2007)CrossRef
45.
Wilson, K., Snavely, N.: Robust global translations with 1DSfM. In: European Conference on Computer Vision, pp. 61–75. Springer (2014)
46.
47.
Wu, C.: Towards linear-time incremental structure from motion. In: 3DV (2013)
48.
Yang, Y., Ramanan, D.: Articulated human detection with flexible mixtures of parts. IEEE Trans. Pattern Anal. Mach. Intell. 35(12), 2878–2890 (2013)CrossRef
49.
Zhang, Z.: A flexible new technique for camera calibration. IEEE TPAMI 22(11), 1330–1334 (2000)CrossRef
Metadata
Title
An Image Dataset of Bishnupur Terracotta Temples for Digital Heritage Research
Authors
Mrinmoy Ghorai
Sanchayan Santra
Soumitra Samanta
Pulak Purkait
Bhabatosh Chanda
Copyright Year
2018
Publisher
Springer Singapore
Book
Heritage Preservation

Print ISBN: 978-981-10-7220-8

Electronic ISBN: 978-981-10-7221-5

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-981-10-7221-5_13

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