All Issue

2020 Vol.52, Issue 2 Preview Page
30 April 2020. pp. 43-51
Abstract
References

Literature Cited

1

Wen, J. S. and Wei, T., Multivariable dimension-reduction and synergic control strategy for cross-directional basis weight of papermaking process, Journal of Korea TAPPI 51(2):76-87 (2019).

10.7584/JKTAPPI.2019.04.51.2.76
2

Li, Q., Wen, H., and Qu, Y. H., Application of edge tracking algorithm to multiple paper defects segmentation, China Pulp & Paper 36(8):41-45 (2017).

3

Qu, Y. H., Tang, W., and Wen, H., Paper defects de-noising algorithm based on homomorphic filtering and discrete cosine transform, China Pulp & Paper 37(5):45-49 (2018).

4

Zhou, Q., Zhang, H., and Yang, Y. Y., Study on the on-line detection of paper defects using twice 2D daubechies wavelet transformation, Transactions of China Pulp and Paper 29(3):47-53 (2014).

5

Zhou, Q., Chen, Y., and Shen, T. Y., Review of paper defect detection system based on machine vision technology, China Pulp & Paper 35(5):72 (2016).

6

Wang, B., Tang, W., Dong, J. X., and Wang, F., Study on the drive parameters of a high precision basis weight control valve, Journal of Korea TAPPI 49(3):41-56 (2017).

10.7584/JKTAPPI.2017.06.49.3.41
7

Liu, Y. and Wu, X. J., Image recognition algorithm based on Log-Gabor wavelet and Riemannian manifold learn, PR & AI 28(10): 946-952 (2015).

8

Sivabalan, K. N., Efficient defect detection algorithm for gray level digital images using Gabor wavelet filter and Gaussian filter, International Journal of Engineering Science and Technology (IJEST) 4(3):3195-3202 (2011).

9

Qu, Y. H., Tang, W., and Wen, H., An algorithm for low contrast paper defects inspection based on Gabor filter, Transactions of China Pulp and Paper 34(2):42-46 (2019).

10

Han, Z. B., Lin, T., and Yang, K., Research on the SAR image segmentation method based on improved two-dimension ostu arithmetic, Journal of Naval Aeronautical and Astronautical University 25(2):153-157 (2010).

11

Liu, Q. and Zhuang, J. J., A generalized thresholding algorithm of pedestrian segmentation for far-infrared images, Proceedings of IEEE International Conference on Imaging Systems and Techniques (IST), IEEE, Manchester, UK, pp. 338-343 (2012).

10.1109/IST.2012.6295515
12

Karaboga, D. An idea based on honey bee swarm for numerical optimization, Technical Report, Erciyes University, Keyseri, Turkey, pp. 29-38 (2005).

13

Xie, M., Image thresholding segmentation based on multi-objective artificial bee colony optimization, DIGITAL VIDEO 42(3):6-14 (2018).

14

Wei, P. Y., Pan, F. C., and Li, S., Study on classification of improved artificial bee colony algorithm to optimization of BP neural network, Computer Engineering and Applications 54(10):158-163 (2018).

15

Li, H. Y. and He, H. Z., Improved artificial bee colony and K-means clustering for image segmentation, Intelligent Computer and Applications 8(3):45-49 (2018).

16

Gao, Y., Li, X., and Dong, M., An enhanced artificial bee colony optimizer and its application to multi-level threshold image segmentation, Journal of Central South University 25(1):107-120 (2018).

10.1007/s11771-018-3721-z
Information
  • Publisher :Korea Technical Association of The Pulp and Paper Industry
  • Publisher(Ko) :한국펄프종이공학회
  • Journal Title :Journal of Korea TAPPI
  • Journal Title(Ko) :펄프종이기술
  • Volume : 52
  • No :2
  • Pages :43-51
  • Received Date : 2019-12-05
  • Revised Date : 2020-04-05
  • Accepted Date : 2020-04-10