Abstract
Active contour model has been a widely used methodology in image segmentation. However, due to the texture complexity of natural images, it unavoidably faces many difficulties. In this paper, we propose a novel method to accurately segment natural images by texture and high order edge-detector. Firstly, we calculate local covariance matrix which is estimated from image gradient information within the local window, and use the eigenvalues of matrix to describe local texture feature of the image. Then, in order to suppress the effect of perplexing background, we introduce a high order edge-detector which can eliminate the background as much as possible while it can save the object boundary. Finally, the intensity term, texture term and edge-detector term are incorporated into the level set method to segment natural images. The proposed method has been tested on many natural images, and experimental results show the segmentation performance of the proposed method is better than prior similar state-of-the-art methods.
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References
Chan, T., Vese, L.: Active contours without edges. IEEE Trans. Image Process. 10(2), 266–277 (2001)
Li, C., Kao, C., John, C., Ding, Z.: Minimization of region-scalable fitting energy for image segmentation. IEEE Trans. Image Process. 17(10), 1940–1949 (2008)
Zhang, K., Song, H., Zhang, L.: Active contours driven by local image fitting energy. Pattern Recogn. 43(4), 1199–1206 (2010)
Wang, L., He, L., Mishra, A., Li, C.: Active contours driven by local Gaussian distribution fitting energy. Sign. Process. 89(12), 2435–2447 (2009)
Li, C., Huang, R., Ding, Z., Gatenby, J., Metaxas, D., Gore, J.: A level set method for image segmentation in the presence of intensity inhomogeneities with application to MRI. IEEE Trans. Image Process. 20(7), 2007–2016 (2011)
Zhang, K., Zhang, L., Lam, K., Zhang, D.: A level set approach to image segmentation with intensity inhomogeneity. IEEE Trans. Cybern. 46(2), 546–557 (2016)
Min, H., Zhao, Y., Zuo, W., Ling, H., Luo, Y.: LATE: a level-set method based on local approximation of Taylor expansion for segmenting intensity inhomogeneous image. IEEE Trans. Image Process. 27(10), 5016–5031 (2018)
Liu, J., Wei, X., Li, Q., Li, L.: A level set algorithm based on probabilistic statistics for MR image segmentation. In: International Conference on Intelligence Science and Big Data Engineering (IScIDE), pp. 577–586 (2018)
Wang, X., Huang, D., Xu, H.: An efficient local Chan-Vese model for image segmentation. Pattern Recogn. 43(3), 603–618 (2010)
Wang, B., Gao, X., Tao, D., Li, X.: A unified tensor level set for image segmentation. IEEE Trans. Syst. Man Cybern.-Part B: Cybern. 40(3), 857–867 (2010)
Dai, L., Ding, J., Yang, J.: Inhomogeneity-embedded active contour for natural image segmentation. Pattern Recogn. 48(8), 2513–2529 (2015)
Min, H., Lu, J., Jia, W., Zhao, Y., Luo, Y.: An effective local regional model based on salient fitting for image segmentation. Neurocomputing 311(15), 245–259 (2018)
Kim, W., Kim, C.: Active contours driven by the salient edge energy model. IEEE Trans. Image Process. 22(4), 1667–1673 (2013)
Getreuer, P.: Chan-Vese segmentation. Image Process. Line 2, 214–224 (2012)
Seo, H., Milanfar, P.: Static and space-time visual saliency detection by self-resemblance. J. Vis. 9(12), 1–27 (2009)
Achanta, R., Hemami, S., Estrada, F.: Frequency-tuned saliency region detection. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1597–1604 (2009)
Berkeley Segmentation Dataset 500. https://www2.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/resources.html
Acknowledgements
This work is supported by National Natural Science Foundation of China (NSFC) (61501260, 61471201, 61471203), Jiangsu Province Higher Education Institutions Natural Science Research Key Grant Project (13KJA510004), The peak of six talents in Jiangsu (RLD201402), and “1311 Talent Program” of NJUPT.
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Yao, Y., Cui, Z., Liu, F. (2019). A Level Set Method for Natural Image Segmentation by Texture and High Order Edge-Detector. In: Cui, Z., Pan, J., Zhang, S., Xiao, L., Yang, J. (eds) Intelligence Science and Big Data Engineering. Visual Data Engineering. IScIDE 2019. Lecture Notes in Computer Science(), vol 11935. Springer, Cham. https://doi.org/10.1007/978-3-030-36189-1_32
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DOI: https://doi.org/10.1007/978-3-030-36189-1_32
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