DOI QR코드

DOI QR Code

A Method for Extracting Mosaic Blocks Using Boundary Features

경계 특징을 이용한 모자이크 블록 추출 방법

Jang, Seok-Woo;Park, Young-Jae;Huh, Moon-Haeng
장석우;박영재;허문행

  • Received : 2015.08.25
  • Accepted : 2015.10.05
  • Published : 2015.12.31

Abstract

Recently, with the sharp increase of digital visual media such as photographs, animations, and digital videos, it has been necessary to generate mosaic blocks in a static or dynamic image intentionally or unintentionally. In this paper, we suggest a new method for detecting mosaic blocks contained in a color image using boundary features. The suggested method first extracts Canny edges in the image and finds candidate mosaic blocks with the boundary features of mosaic blocks. The method then determines real mosaic blocks after filtering out non-mosaic blocks using geometric features like size and elongatedness features. Experimental results show that the proposed method can detect mosaic blocks robustly rather than other methods in various types of input images.

Keywords

Feature Extraction;Edge;Filtering;Mosaic Block;Candidate Region

References

  1. J. Liu, L. Huang, and J. Lin, "An image mosaic block detection method based on fuzzy c-means clustering," in Proceedings of the IEEE International Conference on Computer Research and Development (ICCRD), vol. 1, pp. 237-240, March 2011.
  2. S. Nashat, A. Abdullah, and M. Z. Abdullah, "Unimodal thresholding for Laplacian-based Canny-Deriche filter," Pattern Recognition Letters, vol. 33, no. 10, pp. 1269-1286, July 2012. https://doi.org/10.1016/j.patrec.2012.03.023
  3. M. Sezgin and B. Sankur, "Survey over image thresholding techniques and quantitative performance evaluation," Journal of Electronic Imaging, vol. 13, no. 1, pp. 146-165, January 2004. https://doi.org/10.1117/1.1631315
  4. X.-C. Yuan, L.-S. Wu, and Q. Peng "An improved Otsu method using the weighted object variance for defect detection," Applied Surface Science, vol. 349, pp. 472-484, September 2015. https://doi.org/10.1016/j.apsusc.2015.05.033
  5. B. Gan, T. Menkhoff, and R. Smith, "Enhancing students' learning process through interactive digital media: New opportunities for collaborative learning," Computers in Human Behavior, vol. 51, Part B, pp. 652-663, October 2015. https://doi.org/10.1016/j.chb.2014.12.048
  6. S. Lee, S. Rho, and J. H. Park, "Multimedia contents adaptation by modality conversion with user preference in wireless network," Journal of Network and Computer Applications, vol. 37, pp. 25-32, January 2014. https://doi.org/10.1016/j.jnca.2011.03.034
  7. S.-F. Sun, S.-H. Han, G. Wang, Y.-C. Xu, and B.-J. Lei, "Mosaic defect detection in digital video," in Proceedings of the IEEE Chinese Conference on Pattern Recognition (CCPR), pp. 1-5, October 2010.
  8. Y.-J. Park and G.-Y. Kim, "A study on detection of mosaic in adult image," in Proceedings of the Korea Society of Computer and Information Conference, vol. 22, no. 1, pp. 63-64, 2014.
  9. D. Guo, J. Tang, Y. Cui, J. Ding, and C. Zhao, "Saliencybased content-aware lifestyle image mosaics," Journal of Visual Communication and Image Representation, vol. 26, pp. 192-199, January 2015. https://doi.org/10.1016/j.jvcir.2014.11.011
  10. X. Huang, H. Ma, and H. Yuan, "Video mosaic block detection based on template matching and SVM," in Proceedings of the IEEE International Conference for Young Computer Scientist (ICYCS), pp. 1082-1086, November 2008.
  11. Z. Wei, J. Lin, L. Zhang, and S. Song, "Mosaic defect detection based on macro block solid edge detection," Research Journal of Applied Science, Engineering and Technology, no. 5, vol. 13, pp. 3549-3553, April 2013.