Abstract
The rejoining of oracle bone rubbings is a fundamental topic for oracle research. However, it is a tough task to reassemble severely broken oracle bone rubbings because of detail loss in manual labeling, the great time consumption of rejoining, and the low accuracy of results. To overcome the challenges, we introduce a novel CFDA&CAP algorithm that consists of the Curve Fitting Degree Analysis (CFDA) algorithm and the Correlation Analysis of Pearson (CAP) algorithm. First, the orthogonalization system is constructed to extract local features based on the curve features analysis. Second, the global feature descriptor is depicted by using coordinate points sequences. Third, we screen candidate curves based on the features as well as the CFDA algorithm, so the search range of the candidates is narrowed down. Finally, image recommendation libraries for target curves are generated by adopting the CAP algorithm, and the rank for each target matching curve generates simultaneously for result evaluation. With experiments, the proposed method shows a good effect in rejoining oracle bone rubbings automatically: (1) it improves the average accuracy rate of curve matching up to 84%, and (2) for a low-resource task, the accuracy of our method has 25% higher accuracy than that of other methods.
- M. Baak, R. Koopman, H. Snoek, and S. Klous. 2020. A new correlation coefficient between categorical, ordinal and interval variables with Pearson characteristics. Computational Statistics & Data Analysis 152 (Dec. 2020), 107043. DOI:https://doi.org/10.1016/j.csda.2020.107043Google Scholar
- S. Belongie, J. Malik, and J. Puzicha. 2002. Shape matching and object recognition using shape contexts. IEEE Transactions on Pattern Analysis and Machine Intelligence 24, 4 (April 2002), 509–522. DOI:https://doi.org/10.1109/34.993558 Google Scholar
Digital Library
- S. Caggiano, M. De Marsico, R. Distasi, and D. Riccio. 2015. MOSAIC: Multi-object segmentation for assisted image ReConstruction. In Pattern Recognition: Applications and Methods, Ana Fred, Maria De Marsico, and Mário Figueiredo (Eds.). Springer International, Lisbon, Portugal, 282–299. DOI:https://doi.org/10.1007/978-3-319-27677-9_18 Google Scholar
Digital Library
- Qinsheng Chen, M. Defrise, and F. Deconinck. 1994. Symmetric phase-only matched filtering of Fourier-Mellin transforms for image registration and recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 16, 12 (Dec. 1994), 1156–1168. DOI:https://doi.org/10.1109/34.387491 Google Scholar
Digital Library
- Shanxiong Chen, Han Xu, Gao Weize, Liu Xuxin, and Mo Bofeng. 2020. A classification method of oracle materials based on local convolutional neural network framework. IEEE Computer Graphics & Applications 40, 3 (May 2020), 32–44. DOI:https://doi.org/10.1109/MCG.2020.2973109Google Scholar
Cross Ref
- E. Bribiesca. 1999. A new chain code. Pattern Recognition 32, 2 (Feb. 1999), 235–251. DOI:https://doi.org/10.1016/S0031-3203(98)00132-0Google Scholar
Cross Ref
- H. Freeman. 1961. Techniques for the digital computer analysis of chain-encoded arbitrary plane curves. In Proceedings of the National Electronics Conference. 421–432.Google Scholar
- H. Freeman. 1974. Computer processing of line-drawing images. ACM Computing Surveys 6, 1 (March 1974), 57–97. DOI:https://doi.org/10.1145/356625.356627 Google Scholar
Digital Library
- Yuhua Gu and Tardi Tjahjadi. 2000. Coarse-to-fine planar object identification using invariant curve features and B-spline modeling. Pattern Recognition 33, 9 (Sept. 2000), 1411–1422. DOI:https://doi.org/10.1016/S0031-3203(99)00131-4Google Scholar
Cross Ref
- Jun Guo, Changhu Wang, E. Roman-Rangel, Hongyang Chao, and Yong Rui. 2016. Building hierarchical representations for oracle character and sketch recognition. IEEE Transactions on Image Processing 25, 1 (Jan. 2016), 104–118. DOI:https://doi.org/10.1109/TIP.2015.2500019Google Scholar
Digital Library
- Moruo Guo and Houxuan Hu. 1999. The Collection of Oracle Bone Inscriptions. Zhong Hua Book Company, Beijing, China.Google Scholar
- Tianshu Huang. 2010. A Collection of Bones and Tortoise Shells. Cambridge University Press, Cambridge, MA.Google Scholar
- Changjiang Jin, Mingfu Li, Jin Wang, and Xiupeng Liu. 2017. A representation and matching algorithm for planar curve based on distance ratio and concentric circles. Procedia Computer Science 107 (April 2017), 361–366. DOI:https://doi.org/10.1016/j.procs.2017.03.118 Google Scholar
Digital Library
- P. Karczmarek, A. Kiersztyn, W. Pedrycz, and P. Rutka. 2016. Chain code-based local descriptor for face recognition. In Proceedings of the 9th International Conference on Computer Recognition Systems CORES 2015, Vol. 403. Springer International, Polanica Zdroj, Poland, 307–316. DOI:https://doi.org/10.1007/978-3-319-26227-7_29Google Scholar
- D. A. Kosiba, P. M. Devaux, S. Balasubramanian, T. L. Gandhi, and K. Kasturi. 1994. An automatic jigsaw puzzle solver. In Proceedings of the 12th International Conference on Pattern Recognition. 616–618. DOI:https://doi.org/10.1109/ICPR.1994.576377Google Scholar
Cross Ref
- H. Li, Y. Zheng, S. Zhang, and J. Cheng. 2014. Solving a special type of jigsaw puzzles: Banknote reconstruction from a large number of fragments. IEEE Transactions on Multimedia 16, 2 (Feb. 2014), 571–578. DOI:https://doi.org/10.1109/TMM.2013.2291968 Google Scholar
Digital Library
- Xiaoming Li, Xunpo Zhao, Lian Zheng, and Zhanyi Hu. 2006. An image registration technique based on Fourier-Mellin transform and its extended applications. Chinese Journal of Computers 29, 3 (March 2006), 466–472.Google Scholar
- Shuang Liu, Yanjuan Zhu, and Liyan Zhang. 2005. Research on algorithm for matching 2D contours. Electrical Technology & Automation 34, 2 (April 2005), 60–63.Google Scholar
- Yashuang Mu, Xiaodong Liu, and Lidong Wang. 2018. A Pearson's correlation coefficient based decision tree and its parallel implementation. Information Sciences 435 (April 2018), 40–58. DOI:https://doi.org/10.1016/j.ins.2017.12.059Google Scholar
- C. Papaodysseus, T. Panagopoulos, M. Exarhos, C. Triantafillou, D. Fragoulis, and C. Doumas. 2002. Contour-shape based reconstruction of fragmented, 1600 BC wall paintings. IEEE Transactions on Signal Processing 50, 6 (June 2002), 1277–1288. DOI:https://doi.org/10.1109/TSP.2002.1003053 Google Scholar
Digital Library
- Bangjiong Peng, Ji Xie, and Jifan Ma. 1999. Supplement to the Collection of Oracle Bone Inscription. Language Press, Beijing, China.Google Scholar
- B. S. Reddy and B. N. Chatterji. 1996. An FFT-based technique for translation, rotation, and scale-invariant image registration. IEEE Transactions on Image Processing 5, 8 (Aug. 1996), 1266–1271. DOI:https://doi.org/10.1109/83.506761 Google Scholar
Digital Library
- M. S. Sagiroglu and A. Ercil. 2006. A texture based matching approach for automated assembly of puzzles. In Proceedings of the 18th International Conference on Pattern Recognition (ICPR'06), Vol. 3. 1036–1041. DOI:https://doi.org/10.1109/ICPR.2006.184 Google Scholar
Digital Library
- M. Tewari, S. Jana, and R. Parekh. 2017. An automated system for image reconstruction from distorted image fragments. In Proceedings of the 2017 International Conference on Computer, Electronics, and Communication Engineering (ICCECE'17). 1–6. DOI:https://doi.org/10.1109/ICCECE.2017.8526211Google Scholar
- Borut Žalik, Domen Mongus, Yong-Kui Liu, and Niko Lukač. 2016. Unsigned Manhattan chain code. Journal of Visual Communication and Image Representation 32, 2 (July 2016), 186–194. DOI:https://doi.org/10.1016/j.jvcir.2016.03.001 Google Scholar
Digital Library
- A. Wang, Y. Ge, and G. Liu. 2010. Research on key technologies of the computer aided rejoining of Oracle Bone Inscriptions. In Proceedings of the 2010 2nd IEEE International Conference on Information and Financial Engineering. IEEE, Los Alamitos, CA, 180–183. DOI:https://doi.org/10.1109/ICIFE.2010.5609279Google Scholar
Cross Ref
- Guowei Wang. 1959. Collected Works of Guan-Tang. Zhong Hua Book Company, Beijing, China.Google Scholar
- Ke Wang, Huiqin Wang, Yin Yin, Li Mao, and Yi Zhang. 2018. Time series prediction method based on Pearson correlation BP neural network. Optics and Precision Engineering 26, 11 (Nov. 2018), 2805–2813. DOI:https://doi.org/10.3788/OPE.20182611.2805Google Scholar
- L. Wang and D. Rajan. 2020. An image similarity descriptor for classification tasks. Journal of Visual Communication and Image Representation 71 (Aug. 2020), 102847. DOI:https://doi.org/10.1016/j.jvcir.2020.102847Google Scholar
Cross Ref
- Hui Wei and Lirong Li. 2017. Curve description and matching using arch sequence. Journal of Image and Graphics 22, 8 (Aug. 2017), 1045–1055. DOI:https://doi.org/10.11834/jig.170041Google Scholar
- Wu Youguang, Xin Li, and Maoqing Li. 2013. Color and contour based reconstruction of fragmented image. In Proceedings of the 2013 8th International Conference on Computer Science Education. 999–1003. DOI:https://doi.org/10.1109/ICCSE.2013.6554059Google Scholar
Cross Ref
- Haichao Zhang, Yatao Wang, and Fangfang Zhang. 2011. Efficient matching algorithm for 3D contour curve. Computer Engineering 37, 8 (April 2011), 228–230. DOI:https://doi.org/10.3969/j.issn.1000-3428.2011.08.079Google Scholar
- Yanchao Zhao and Chen Yang. 2015. The compilation of oracle-bone archives. Archives Science Study1 (Feb. 2015), 120–128. DOI:https://doi.org/10.16065/j.cnki.issn1002-1620.2015.01.024Google Scholar
- Haomiao Zhou, Zhihong Deng, Yuanqing Xia, and Mengyin Fu. 2016. A new sampling method in particle filter based on Pearson correlation coefficient. Neurocomputing 216, 5 (Dec. 2016), 208–215. DOI:https://doi.org/10.1016/j.neucom.2016.07.036 Google Scholar
Digital Library
- Jun Zhou, Haozhou Yu, Karen Smith, Colin Wilder, Hongkai Yu, and Song Wang. 2017. Identifying designs from incomplete, fragmented cultural heritage objects by curve-pattern matching. Journal of Electronic Imaging 26, 1 (Jan. 2017), 1–15. DOI:https://doi.org/10.1117/1.JEI.26.1.011022Google Scholar
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The Research on Rejoining of the Oracle Bone Rubbings Based on Curve Matching
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