Skip to main content
Top

2018 | OriginalPaper | Chapter

Automatic Verification Framework of 3D Scan Data for Museum Collections

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

3D digital archiving of cultural heritage has been conducting actively all over the world. Although the applications using the obtained digital 3D scan data are widely developed, their data management and quality verification does not conducted properly. To overcome this problem, we propose a novel verification framework based on the comparisons of shape and color information between an original image and an image from 3D scan data (i.e., mesh data with color mapping). Firstly, to verify that they are the identical object, we use the shape contexts information based on a machine learning technique. Secondly, we compare the color information between them for verifying its quality of color mapping. Utilizing the proposed framework, we expect that non-experts can verify the quality of 3D scan data automatically, thus, museum itself will be able to manage the 3D scan data systematically and reliably.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Literature
1.
go back to reference Tian, D., Gong, M., Su, L.: Generalized correlation for shape alignment. Inf. Sci. 363, 40–51 (2016)CrossRef Tian, D., Gong, M., Su, L.: Generalized correlation for shape alignment. Inf. Sci. 363, 40–51 (2016)CrossRef
2.
go back to reference Sandhu, R., Dambreville, S., Tannenbaum, A.: Point set registration via particle filtering and stochastic dynamics. IEEE Trans. Pattern Anal. Mach. Intell. 32(8), 1459–1473 (2010)CrossRef Sandhu, R., Dambreville, S., Tannenbaum, A.: Point set registration via particle filtering and stochastic dynamics. IEEE Trans. Pattern Anal. Mach. Intell. 32(8), 1459–1473 (2010)CrossRef
3.
go back to reference Li, H., Shen, T., Huang, X.: Approximately global optimization for robust alignment of generalized shapes. IEEE Trans. Pattern Anal. Mach. Intell. 33(6), 1116–1131 (2011)CrossRef Li, H., Shen, T., Huang, X.: Approximately global optimization for robust alignment of generalized shapes. IEEE Trans. Pattern Anal. Mach. Intell. 33(6), 1116–1131 (2011)CrossRef
4.
go back to reference Dong, Z., Yang, B., Liu, Y., Liang, F., Li, B., Zang, Y.: A novel binary shape context for 3D local surface description. ISPRS J. Photogramm. Remote. Sens. 130, 431–452 (2017)CrossRef Dong, Z., Yang, B., Liu, Y., Liang, F., Li, B., Zang, Y.: A novel binary shape context for 3D local surface description. ISPRS J. Photogramm. Remote. Sens. 130, 431–452 (2017)CrossRef
5.
go back to reference Mehtre, B.M., Kankanhalli, M.S., Desai Narasimhalu, A., Chang Man, G.: Color matching for image retrieval. Pattern Recognit. Lett. 16(3), 325–331 (1995)CrossRef Mehtre, B.M., Kankanhalli, M.S., Desai Narasimhalu, A., Chang Man, G.: Color matching for image retrieval. Pattern Recognit. Lett. 16(3), 325–331 (1995)CrossRef
6.
go back to reference Pang, G., Zhu, M., Zhou, P.: Color transfer and image enhancement by using sorting pixels comparison. Optik 126(23), 3510–3515 (2015)CrossRef Pang, G., Zhu, M., Zhou, P.: Color transfer and image enhancement by using sorting pixels comparison. Optik 126(23), 3510–3515 (2015)CrossRef
7.
go back to reference Zhang, S., Zhan, Y., Dewan, M., Huang, J., Metaxas, D.N., Zhou, X.S.: Towards robust and effective shape modeling: sparse shape composition. Med. Image Anal. 16(1), 265–277 (2012)CrossRef Zhang, S., Zhan, Y., Dewan, M., Huang, J., Metaxas, D.N., Zhou, X.S.: Towards robust and effective shape modeling: sparse shape composition. Med. Image Anal. 16(1), 265–277 (2012)CrossRef
8.
go back to reference Belongie, S., Malik, J., Puzichal, J.: Shape context: a new descriptor for shape matching and object recognition. Adv. Neural. Inf. Process. Syst. 2001, 831–837 (2001) Belongie, S., Malik, J., Puzichal, J.: Shape context: a new descriptor for shape matching and object recognition. Adv. Neural. Inf. Process. Syst. 2001, 831–837 (2001)
9.
go back to reference Kim, H., Jung, K., Chang, M., Kim, J.: Feature detection using measured 3D data and image data. J. Korean Soc. Precis. Eng. 30(6), 601–606 (2013). No. 267CrossRef Kim, H., Jung, K., Chang, M., Kim, J.: Feature detection using measured 3D data and image data. J. Korean Soc. Precis. Eng. 30(6), 601–606 (2013). No. 267CrossRef
10.
go back to reference Cárdenas-Peña, D., Collazos-Huertas, D., Álvarez-Meza, A., Castellanos-Dominguez, G.: Supervised kernel approach for automated learning using general stochastic networks. Eng. Appl. Artif. Intell. 68, 10–17 (2018)CrossRef Cárdenas-Peña, D., Collazos-Huertas, D., Álvarez-Meza, A., Castellanos-Dominguez, G.: Supervised kernel approach for automated learning using general stochastic networks. Eng. Appl. Artif. Intell. 68, 10–17 (2018)CrossRef
11.
go back to reference Yoo, H.J., Kim, K.Y., Kim, H.M., Seo, M.K., Ko, K.H., Lee, K.H.: Realistic representation based on measured BRDF data. In: HCI KOREA, pp. 1019–1024, 5 February 2007 Yoo, H.J., Kim, K.Y., Kim, H.M., Seo, M.K., Ko, K.H., Lee, K.H.: Realistic representation based on measured BRDF data. In: HCI KOREA, pp. 1019–1024, 5 February 2007
12.
go back to reference Willcocks, C.G., Jackson, P.T.G., Nelson, C.J., Obara, B.: Extracting 3D parametric curves from 2D images of helical objects. IEEE Trans. Pattern Anal. Mach. Intell. 39(9), 1757–1769 (2017)CrossRef Willcocks, C.G., Jackson, P.T.G., Nelson, C.J., Obara, B.: Extracting 3D parametric curves from 2D images of helical objects. IEEE Trans. Pattern Anal. Mach. Intell. 39(9), 1757–1769 (2017)CrossRef
13.
go back to reference Guarnera, D., Guarnera, G.C., Ghosh, A., Denk, C., Glencross, M.: BRDF representation and acquisition. Comput. Graph. Forum: J. Eur. Assoc. Comput. Graph. 35(2), 625–650 (2016)CrossRef Guarnera, D., Guarnera, G.C., Ghosh, A., Denk, C., Glencross, M.: BRDF representation and acquisition. Comput. Graph. Forum: J. Eur. Assoc. Comput. Graph. 35(2), 625–650 (2016)CrossRef
14.
go back to reference Havran, V., Filip, J., Myszkowski, K.: Perceptually motivated BRDF comparison using single image. Comput. Graph. Forum: J. Eur. Assoc. Comput. Graph. 35(4), 1–12 (2016)CrossRef Havran, V., Filip, J., Myszkowski, K.: Perceptually motivated BRDF comparison using single image. Comput. Graph. Forum: J. Eur. Assoc. Comput. Graph. 35(4), 1–12 (2016)CrossRef
Metadata
Title
Automatic Verification Framework of 3D Scan Data for Museum Collections
Authors
Jeong-eun Oh
Jeongmin Yu
Copyright Year
2018
DOI
https://doi.org/10.1007/978-3-030-01765-1_30