This paper discusses the binarization restoration algorithms of ancient Chinese Rubbings image. According to the location of the image features, such as color, edge, stroke width and pixel location feature, the binary restoration algorithm of digital rubbings based on threshold segmentation and morphology is discussed. If the difference between the text and the background is larger and more obvious distinguish in images, Otsu threshold segmentation method can be directly used. For images with more background noise, shadows and uneven illumination, threshold segmentation cannot achieve effective segmentation and reasonable denoising, a set of algorithms associated with the pixel position of adaptive segmentation, morphological method, connected domain and mean filtering combined of binarization restoration are needed. The experimental results show that this algorithm is very effective for image with complex and large area noise.
XuX.Z.ShiZ.Z. and JiaW.K., New theory and new method of image segmentation, Journal of Electronics38(S1) (2010), 76–82.
2.
MaY.L.HuangZ.K. et al., Research on Image Segmentation of Digital Rubbings Based on OTSU Threshold and Genetic Algorithm, in: 2017 5th International Conference on Mechanical, Automotive and Materials Engineering (CMAME), 2017.
3.
ZhouD. and XiaZ.L., An improved Otsu threshold segmentation algorithm, Journal of China Jiliang University27(3) (2016), 319–323.
4.
YangX. and LuY., Poor grey image threshold operator and improved Otsu algorithm combined with the segmentation and implementation, Instrument Technique and Sensor Research (3) (2015), 104–106.
5.
ZhangH.GaoM. and XuC., Image segmentation algorithm based on fusion threshold and edge detection, Journal of the Academy of Armored Forces Engineering (5) (2014), 73–76.
6.
ZhangZ.Q. and SongH.S., Application of Otsu improved Canny operator image edge detection method, Computer and Digital Engineering42(1) (2014), 122–128.
7.
HuM. and CaiH.F., Watershed image segmentation based on morphological mark connected, Journal of Electronic Measurement and Instrument25(25) (2011), 864–869.
8.
LiL.FanJ.L. and ZhaoF., An improved image segmentation algorithm based on C-mean clustering, Journal of Xi’an University of Posts and Telecommunications (5) (2014), 56–60.
9.
ZhaoZ.ChengL. and ChengG., Neighbourhood weighted fuzzy C-means clustering algorithm for image segmentation, Iet Image Processing8(3) (2014), 150–161.
10.
ZhongC.F. and LiuZ., An improved image segmentation algorithm based on Genetic Fuzzy C mean clustering, Journal of Changchun University of Science and Technology (Natural Science Edition) (2) (2014), 62–67.
11.
WuY.YinJ.DaiY. and YuanY., Identification method of freshwater fish species using multi-kernel support vector machine with bee colony optimization, Transactions of the Chinese Society of Agricultural Engineering30(16) (2014), 312–319.
12.
ZhouP.F. and ZhangY.Q., Research on image segmentation method based on fuzzy BP neural network, Computer Engineering and Design (7) (2014), 2423–2426.
13.
ZhouP.F., Research on image segmentation based on Improved Fuzzy BP neural network, Taiyuan University of Technology, 2014.
14.
CaiH.YangZ.CaoX.XiaW. and XuX., A new iterative triclass thresholding technique in image segmentation, IEEE Trans Image Process23(3) (2014), 1038–1046.
15.
YuanX.C.WuL.S. and PengQ., An improved Otsu method using the weighted object variance for defect detection, Applied Surface Science349 (2015), 472–484.
16.
AdhikariG. and DasguptaT., A Local Adaptive Region-wise Histogram Correction and Thresholding Technique for Very Poorly Illuminated Images, IEEE, 2018.
17.
LiangY.LinZ. et al., eds. Document image binarization via optimized hybrid thresholding, in: IEEE International Symposium on Circuits & Systems, 2017.
18.
IngleP.D. and KaurP., eds. Adaptive thresholding to robust image binarization for degraded document images, in: International Conference on Intelligent Systems & Information Management, 2017.
19.
VijayaraniM.A.S.D.S., A hybrid approach for document image binarization, in: Proceedings of the International Conference on Inventive Computing and Informatics (ICICI 2017), IEEE Xplore Compliant – Part Number, CFP17L34-ART.
20.
WangH.X.LiR.F.GaoY.F.CaoC.Q.GeL.Z. and XieX., Target Recognition and Localization of Mobile Robot with Monocular PTZ Camera, Journal of Robotics, 2019.
21.
MaY.L. and HuangZ.K., The study of segmentation of rubbings image based on OTSU & histogram differential algorithm, in: International Conference on Computer, Information and Telecommunication Systems (CITS), 2017.
22.
ChutaniG.PatnaikT. and DwivediV., An improved approach for automatic denoising and binarization of degraded document images based on region localization, in: International Conference on Advances in Computing, 2015.
23.
XuX.XuS.JinL. and SongE., Characteristic analysis of Otsu threshold and its applications, Pattern Recognition Letters32(7) (2011), 956–961.
24.
HendriksC.L.L.BorgeforsG. and StrandR., Mathematical morphology and its applications, Signal and Image Processing7883(4) (2015), 384.
25.
AbdeldayemA.R.HamouA.K. and ElsakkaM.R., Novel adaptive filtering for salt-and-pepper noise removal from binary document images, Lecture Notes in Computer Science3212 (2004), 191–199.
26.
MaY.L.ZengX. and LiJ., License Plate Location Research Based on Texture Analysis & Mathematics Morphology, Advanced Materials Research, 2011.
