Abstract
Focal blur segmentation is one of the interesting topics in computer vision. With recent improvements of camera devices, multiple focal blur images of different focal settings can be obtained by a single shooting. Utilizing the information of multiple focal blur images is expected to improve the segmentation performance. We propose one of the automatic focal blur segmentation using a pair of two focal blur images with different focal settings. Difference of blur features can be obtained from an image pair which are focused on an object and background, respectively. A theoretical threshold identifies the object and background in the difference of blur feature space. The proposed method consists of (i) the theoretical thresholding in the blur feature space; and (ii) energy minimization based on Graphcuts using color and blur features. We evaluate the proposed method using 12 and 48 image pairs, including single objects and flowers, respectively. As results of the evaluation, the averaged Informedness of the initial and the final segmentation are 0.897 and 0.972 for the single object images, and 0.730 and 0.827 for the flower images, respectively.
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Boykov, Y., Kolmogorov, V.: An experimental comparison of min-cut/max- flow algorithms for energy minimization in vision. IEEE Trans. Pattern Anal. Mach. Intell. 26(9), 1124–1137 (2004)
Chakrabarti, A., Zickler, T., Freeman, W.: Analyzing spatially-varying blur. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 2512–2519, June 2010
Hyeongwoo, K., Christian, R., Christian, T.: Video depth-from-defocus. In: Proceedings of Fourth International Conference on 3D Vision, pp. 370–379, October 2016
Levin, A., Lischinski, D., Weiss, Y.: A closed-form solution to natural image matting. IEEE Trans. Pattern Anal. Mach. Intell. 30(2), 228–242 (2008)
Powers, D.M.W.: Evaluation: from precision, recall and f-measure to ROC, informedness, markedness and correlation. Int. J. Mach. Learn. Technol. 2(1), 37–63 (2011)
Renting, L., Zhaorong, L., Jiaya, J.: Image partial blur detection and classification. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8, June 2008
Shi, J., Xu, L., Jia, J.: Discriminative blur detection features. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 2965–2972, June 2014
Takayama, N., Takahashi, H.: Blur map generation based on local natural image statistics for partial blur segmentation. IEICE Trans. Inf. Syst. E100–D(12), 2984–2992 (2017)
Zhang, W., Cham, W.K.: Single image focus editing. In: Proceedings of IEEE International Conference on Computer Vision Workshops, pp. 1947–1954, September 2009
Yuan, L., Chun, Y.: Automatic segmentation of background defocused nature image. In: Proceedings of 2nd International Congress on Image and Signal Processing, pp. 1–5, October 2009
Yuri, Y.B., Marie-Pierre, J.: Interactive graph cuts for optimal boundary and region segmentation of objects in N-D images. In: Proceedings of IEEE International Conference on Computer Vision, vol. 1, pp. 105–112, July 2001
Zhi, L., Weiwei, L., Liquan, S., Zhongmin, H., Zhaoyang, Z.: Automatic segmentation of focused objects from images with low depth of field. Pattern Recogn. Lett. 31(7), 572–581 (2010)
Zhu, X., Cohen, S., Schiller, S., Milanfar, P.: Estimating spatially varying defocus blur from a single image. IEEE Trans. Image Process. 22(12), 4879–4891 (2013)
Zhuo, S., Sim, T.: Defocus map estimation from a single image. Pattern Recogn. 44(9), 1852–1858 (2011)
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Takayama, N., Takahashi, H. (2020). Automatic Focal Blur Segmentation Based on Difference of Blur Feature Using Theoretical Thresholding and Graphcuts. In: Blanc-Talon, J., Delmas, P., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2020. Lecture Notes in Computer Science(), vol 12002. Springer, Cham. https://doi.org/10.1007/978-3-030-40605-9_29
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DOI: https://doi.org/10.1007/978-3-030-40605-9_29
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