No Access

An Intelligent Computer Assistance System for Artifact Restoration Based on Genetic Algorithms with Plane Image Features

Koji Kashihara

Graduate School of Science and Technology, Tokushima University, 2-1 Minamijyousanjima, Tokushima, 770-8506 Japan

E-mail Address: kojikasi@is.tokushima-u.ac.jp

https://doi.org/10.1142/S1469026817500213Cited by:6 (Source: Crossref)

Abstract

Restoration work of archaeological artifacts broken into pieces is similar to putting together a jigsaw puzzle. The purpose of this study is to construct an intelligent computer assistance system to conveniently restore archaeological discoveries from some fragments. AReal-Coded Genetic Algorithm (RCGA) was applicable for solving the positioning problem of a three-dimensional (3D) restoration. The fitness function value for RCGA was calculated from image similarity between the target and correct patterns in plane images at multiple camera angles. Image features of a 3D object were obtained by the ORB (Oriented FAST and Rotated BRIEF), BRISK (Binary Robust Invariant Scalable Keypoints), and Accelerated KAZE (AKAZE) techniques; they were considered as a part of the fitness function value. Simulation study revealed that the RCGA approach was capable of automatically and efficiently adjusting the positions of 3D fragments, especially in the AKAZE technique. A user interface with the functions of design drawing was also created to assist in repair work. The interactive assistance interface for 3D restoration based on RCGA and followed by the hill-climbing algorithm would be applied to practical applications for digital archives of artifacts.

Keywords:

Remember to check out the Most Cited Articles!
Check out these titles in artificial intelligence!

References

1. M. Kampel and R. Sablatnig, On 3d mosaicing of rotationally symmetric ceramic fragments, in Proc. 17th Int. Conf. Pattern Recognition (ICPR) (IEEE, Cambridge, UK, 2004), pp. 265–268. Google Scholar
2. R. Haliř, An automatic estimation of the axis of rotation of fragments of archaeological pottery: A multi-step model-based approach, in Proc. 7th Int. Conf. Central Europe on Computer Graphics, Visualization and Interactive Digital Media (Univ. of West Bohemia Press, Plzen, Czech Republic, 1999). Google Scholar
3. G. Papaioannou, E. A. Karabassi and T. Theoharis, Automatic reconstruction of archaeological finds — A graphics approach, in Proc. 4th Int. Conf. Computer Graphics and Artificial Intelligence (3IA’00) (Limoges, France, 2000), pp. 117–125. Google Scholar
4. A. Adán, S. Salamanca and P. Merchán, A hybrid human–computer approach for recovering incomplete cultural heritage pieces, Comput. Grap. 36 (1) (2012) 1–15. Crossref, Google Scholar
5. R. Scopigno, 3D models for cultural heritage: Beyond plain visualization, Computer 44 (2011) 48–55. Crossref, Google Scholar
6. M. Kampel and R. Sablatnig, Rule based system for archaeological pottery classification, Pattern Recognit. Lett. 28 (6) (2007) 740–747. Crossref, Google Scholar
7. M. S. Sağiroğlu and A. A. Erçil, Texture based matching approach for automated assembly of puzzles, in Proc. 18th Int. Conf. Pattern Recognition (ICPR) (IEEE, Hong Kong, China, 2006), pp. 1036–1041. Google Scholar
8. I. Saragusti, A. Karasik, I. Sharon and U. Smilansky, Quantitative analysis of shape attributes based on contours and section profiles in artifact analysis, J. Archaeol. Sci. 32 (6) (2005) 841–853. Crossref, Google Scholar
9. P. K. Ray and A. Mahajan, A genetic algorithm-based approach to calculate the optimal configuration of ultrasonic sensors in a 3D position estimation system, Robot. Auton. Syst. 41 (2002) 165–177. Crossref, Google Scholar
10. P. Markelj, D. Tomaževič, B. Likar and F. Pernuš, A review of 3D/2D registration methods for image-guided interventions, Med. Image Anal. 16 (3) (2012) 642–661. Crossref, Google Scholar
11. D. G. Lowe, Object recognition from local scale-invariant features, in Proc. Seventh IEEE Int. Conf. Computer Vision (ICCV) (IEEE, Kerkyra, Greece, 1999), pp. 1150–1157. Google Scholar
12. H. Bay, A. Ess, T. Tuytelaars and L. V. Gool, Speeded-Up Robust Features (SURF), Comput. Vis. Image Underst. 110 (3) (2008) 346–359. Crossref, Google Scholar
13. E. Rosten, R. Porter and T. Drummond, Faster and better: A machine learning approach to corner detection, IEEE Trans. Pattern Anal. Mach. Intell. 32 (1) (2010) 105–119. Crossref, Google Scholar
14. M. Calonder, V. Lepetit, C. Strecha and P. Fua, Brief: Binary robust independent elementary features, in Proc. European Conf. Computer Vision (ECCV) (Springer, Crete, Greece, 2010), pp. 778–792. Google Scholar
15. E. Rublee, V. Rabaud, K. Konolige and G. R. Bradski, ORB: An efficient alternative to sift or surf, in Proc. 2011 IEEE Int. Conf. Computer Vision (ICCV) (IEEE, Barcelona, Spain, 2011), pp. 2564–2571. Google Scholar
16. S. Leutenegger, M. Chli and R. Y. Siegwart, BRISK: Binary robust invariant scalable keypoints, in Proc. 2011 IEEE Int. Conf. Computer Vision (ICCV) (IEEE, Barcelona, Spain, 2011), pp. 2548–2555. Google Scholar
17. P. F. Alcantarilla, J. Nuevo and A. Bartoli, Fast explicit diffusion for accelerated features in nonlinear scale spaces, in British Machine Vision Conference (BMVC) (BMVA Press, Bristol, UK, 2013). Google Scholar
18. L. J. Eshelman and J. D. Schaffer, Real-coded genetic algorithms and interval-schemata, Found. Genet. Algorithms (1993) 187–202. Google Scholar
19. L. Arbace, E. Sonnino, M. Callieri, M. Dellepiane, M. Fabbri, A. I. Idelson and R. Scopigno, Innovative uses of 3D digital technologies to assist the restoration of a fragmented terracotta statue, J. Cult. Herit. 14 (4) (2013) 332–345. Crossref, Google Scholar
20. A. Karasik and U. Smilansky, 3D scanning technology as a standard archaeological tool for pottery analysis: Practice and theory, J. Archaeol. Sci. 35 (2008) 1148–1168. Crossref, Google Scholar
21. G. Palmas, N. Pietroni, P. Cignoni, and R. Scopigno, A computer-assisted constraint-based system for assembling fragmented objects, in Digital Heritage International Congress (Digital Heritage) (IEEE, Marseille, France, 2013), pp. 529–536. Google Scholar
22. K. Zhang, W. Yu, M. Manhein, W. Waggenspack and X. Li, 3D fragment reassembly using integrated template guidance and fracture-region matching, in Proc. 2015 IEEE Int. Conf. Computer Vision (ICCV) (IEEE, Santiago, Chile, 2015), pp. 2138–2146. Google Scholar
23. K. Cordes, L. Grundmann and J. Ostermann, Feature evaluation with high-resolution images, in 16th Int. Conf. Computer Analysis of Images and Patterns (CAIP) (Springer, Valleta, Matta, 2015), pp. 374–386. Google Scholar
24. L. G. Hernandez, L. S. Morera and A. A. Azofra, An interactive genetic algorithm for the unequal area facility layout problem, in Soft Computing Models in Industrial and Environmental Applications, 6th Int. Conf. SOCO (Springer, Salamanca, Spain, 2011), pp. 253–262. Google Scholar
25. C. C. Lai and Y. C. Chen, A user-oriented image retrieval system based on interactive Genetic algorithm, IEEE Trans. Instrum. Meas. 60 (10) (2011) 3318–3325. Crossref, Google Scholar
26. K. Kashihara, Optimal view angles in three-dimensional objects constructed from plane figures as mental images, Int. J. Hum.-Comput. Interact. 27 (2011) 606–619. Crossref, Google Scholar
27. K. Kashihara and Y. Nakahara, Evaluation of task performance during mentally imagining three-dimensional shapes from plane figures, Percept. Mot. Skills 113 (2011) 188–200. Crossref, Google Scholar
28. F. Bruno, S. Bruno, G. De Sensi, M. L. Luchi, S. Mancuso and M. Muzzupappa, From 3D reconstruction to virtual reality: A complete methodology for digital archaeological exhibition, J. Cult. Herit. 11 (1) (2010) 42–49. Crossref, Google Scholar
29. K. Schindler, M. Grabner and F. Leberl, Fast on-site reconstruction and visualization of archaeological finds, in Proc. 19th Int. CIPA Symposium (Antalya, Turkey, 2003), pp. 463–468. Google Scholar