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http://dx.doi.org/10.9717/kmms.2015.18.10.1164

Edge Preserving using HOG Guide Filter for Image Segmentation  

OH, Young-Jin (Dept. of Digital Media, Catholic University of Korea)
Kang, Hang-Bong (Dept. of Digital Media, Catholic University of Korea)
Publication Information
Journal of Korea Multimedia Society / v.18, no.10, 2015 , pp. 1164-1171 More about this Journal
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;
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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.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.
4 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.
5 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.
6 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.   DOI
7 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.   DOI
8 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.   DOI
9 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.
10 A. Criminisi, T. Sharp, and A. Blake, “Geos: Geodesic Image Segmentation,” Proceeding of European Conference on Computer Vision, pp. 99-112, 2008.
11 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.
12 D.R.K. Brownrigg, “The Weighted Median Filter,” Communications of the Association for Computing Machinery, Vol. 27, No. 8, pp. 807-818, 1984.   DOI
13 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.   DOI
14 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.   DOI
15 Q. Zhang, X. Shen, L. Xu, and J. Jia, “Rolling Guidance Filter,” Proceeding of European Conference on Computer Vision, pp. 815-830, 2014.
16 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.
17 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.
18 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.   DOI
19 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.   DOI
20 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.   DOI
21 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.   DOI
22 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.   DOI
23 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.   DOI
24 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.
25 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.
26 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.   DOI