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Edge Preserving using HOG Guide Filter for Image Segmentation
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 Title & Authors
Edge Preserving using HOG Guide Filter for Image Segmentation
OH, Young-Jin; Kang, Hang-Bong;
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 Abstract
The edge preserving method is important for image storage and geometric transformation. In this paper, we propose a new edge preserving method using HOG-Guide filter for image segmentation. In our approach, we extract edge information using gradient histogram to set HOG guide line. Then, we use HOG guide line to smooth image. With two to four iterations of smoothing operations, we finally obtain desirable edge preserved image. Our experimental results showed good performances showing that our proposed method is better than other methods.
 Keywords
Edge Preserving;Image Segmentation;Image Filtering;
 Language
Korean
 Cited by
1.
깊이 슈퍼 픽셀을 이용한 실내 장면의 의미론적 분할 방법,김선걸;강행봉;

한국멀티미디어학회논문지, 2016. vol.19. 3, pp.531-538 crossref(new window)
1.
Semantic Segmentation of Indoor Scenes Using Depth Superpixel, Journal of Korea Multimedia Society, 2016, 19, 3, 531  crossref(new windwow)
 References
1.
C. Tomasi and R. Manduchi, “Bilateral Filtering for Gray and Color Images,” Proceeding of International Conference on Computer Vision, pp. 839-846. 1998.

2.
K. He, J. Sun, and X. Tang, “Guided Image Filtering,” Proceeding of European Conference on Computer Vision, pp. 1-14. 2010.

3.
A. Criminisi, T. Sharp, and A. Blake, “Geos: Geodesic Image Segmentation,” Proceeding of European Conference on Computer Vision, pp. 99-112, 2008.

4.
K. Hur, Y. Baek, and W. Kim, “Halftone Noise Removal in Scanned Images using HOG based Adaptive Smoothing Filter,” Journal of Broadcast Engineering pp. 316-324 2012.

5.
D.R.K. Brownrigg, “The Weighted Median Filter,” Communications of the Association for Computing Machinery, Vol. 27, No. 8, pp. 807-818, 1984. crossref(new window)

6.
J. Shi and J. Malik, “Normalized Cuts and Image Segmentation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 22, No. 8, pp. 888-905, 2000. crossref(new window)

7.
P.F. Felzenszwalb and D.P. Huttenlocher, “Efficient Graph-Based Image Segmentation,” International Journal of Computer Vision, Vol. 59, No. 2, pp. 167-181, 2004. crossref(new window)

8.
Q. Zhang, X. Shen, L. Xu, and J. Jia, “Rolling Guidance Filter,” Proceeding of European Conference on Computer Vision, pp. 815-830, 2014.

9.
A.P. Witkin, “Scale-Space Filtering: A New Approach to Multi-Scale Description,” Proceeding of IEEE International Conference on Acoustics, Speech, and Signal Processing, Vol. 9, pp. 150-153. 1984.

10.
R. Hu, M. Barnard, and J. Collomosse, “Gradient Field Descriptor for Sketch based Retrieval and Localization,” Proceedings of 2010 IEEE 17th International Conference on Image Processing, Vol. 10, pp, 1025-1028. 2010.

11.
A. Buades, C. Bartomeu, and J. Morel, “A Non-local Algorithm for Image Denoising,” Proceeing of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 2, pp.60-65. 2005.

12.
J. Shi and J. Malik, “Normalized Cuts and Image Segmentation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 22, No. 8, pp. 888-905, 2000. crossref(new window)

13.
Y.W. Lim and S.U. Lee, “On the Color Image Segmentation Algorithm based on the Thresholding and the Fuzzy C-means Techniques,” Pattern Recognition, Vol. 23, No. 9, pp. 935-952, 1990. crossref(new window)

14.
A. Khotanzad and A. Bouarfa, “Image Segmentation by a Parallel, Non-parametric Histogram based Clustering Algorithm,” Pattern Recognition, Vol. 23, No. 9, pp. 961-973, 1990. crossref(new window)

15.
S.C. Zhu and A. Yuille, “Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 18, No. 9, pp. 884-900, 1996. crossref(new window)

16.
S. Chen, W. Lin, and C. Chen, “Split-and-Merge Image Segmentation based on Localized Feature Analysis and Statistical Tests,” Journal of Graphical Models and Image Processing, Vol. 53, No. 5, pp. 457-475, 1991. crossref(new window)

17.
L. Grady and E.L. Schwartz, “Isoperimetric Graph Partitioning for Image Segmentation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 28, No. 3, pp. 469-475, 2006. crossref(new window)

18.
Y. Luo, Yi, Marhoon, M. Al Dossary, S. & Alfaraj, M. “Edge-preserving Smoothing and Applications,” The Leading Edge, Vol. 21, No. 2, pp. 136-158, 2002. crossref(new window)

19.
F. Durand and J. Dorsey, “Fast Bilateral Filtering for the Display of High-Dynamic-Range Images,” Association for Computing Machinery Transactions on Graphics, Vol. 21, No. 3, pp. 257-266, 2002.

20.
P. Thévenaz, D. Sage, and M. Unser, “Bi-Exponential Edge-Preserving Smoother,” IEEE Transactions on Image Processing, Vol. 21, No. 9, pp. 3924-3936, 2012. crossref(new window)

21.
B. Stephen, N. Parikh, E. Chu, B. Peleato, and J. Eckstein, “Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers,” Foundations and TrendsR in Machine Learning, Vol. 3, pp.1-122. 2010. crossref(new window)

22.
A. Levin, D. Lischinski, and Y. Weiss, “Colorization using Optimization,” Association for Computing Machinery Transactions on Graphics, Vol. 23, No. 3, pp. 689-694. 2004.

23.
E.H. Spriggs, F.D.L. Torre, and M. Hebert, “Temporal Segmentation and Activity Classification from First-Person Sensing,” Proceeding of IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp. 17-24. 2009.

24.
Abramov, A., Pauwels, K., Papon, J., Wörgötter, F., & Dellen, B. “Real-Time Segmentation of Stereo Videos on a Portable System with a Mobile GPU,” IEEE Transactions on Circuits and Systems for Video Technology, Vol. 22, No. 9, pp. 1292-1305, 2012. crossref(new window)

25.
S. Bae and N. KIM, “Line-Edge Detection Using New 2-D Wavelet Function,” Journal of Korea Multimedia Society, Vol. 8, No. 2, pp. 174-180, 2005.

26.
C.H. Shin, “The Study of Edge Extract Methods Using Improved Detect Mask,” Journal of Korea Multimedia Society, Vol. 12, No. 2, pp. 191-199, 2009.