An Improved Level Set Method to Image Segmentation Based on Saliency
2
조회
0
다운로드
1
인용
0
참고문헌
키워드
초록
In order to improve the edge segmentation effect of the level set image segmentation and avoid the influence ofthe initial contour on the level set method, a saliency level set image segmentation model based on local Renyientropy is proposed. Firstly, the saliency map of the original image is extracted by using saliency detectionalgorithm. And the outline of the saliency map can be used to initialize the level set. Secondly, the local energyand edge energy of the image are obtained by using local Renyi entropy and Canny operator respectively. Atthe same time, new adaptive weight coefficient and boundary indication function are constructed. Finally, thelocal binary fitting energy model (LBF) as an external energy term is introduced. In this paper, the contrastexperiments are implemented in different image database. The robustness of the proposed model forsegmentation of images with intensity inhomogeneity and complicated edges is verified.
Abstract
In order to improve the edge segmentation effect of the level set image segmentation and avoid the influence ofthe initial contour on the level set method, a saliency level set image segmentation model based on local Renyientropy is proposed. Firstly, the saliency map of the original image is extracted by using saliency detectionalgorithm. And the outline of the saliency map can be used to initialize the level set. Secondly, the local energyand edge energy of the image are obtained by using local Renyi entropy and Canny operator respectively. Atthe same time, new adaptive weight coefficient and boundary indication function are constructed. Finally, thelocal binary fitting energy model (LBF) as an external energy term is introduced. In this paper, the contrastexperiments are implemented in different image database. The robustness of the proposed model forsegmentation of images with intensity inhomogeneity and complicated edges is verified.
참고문헌 (19)
- S. Osher, "Fronts propagating with curvature-dependent speed : algorithms based on Hamilton-Jacobi formulations", Journal of Computational Physics, 79(1), pp.12~49 (1988)
- T. F. Chan, "Active contours without edges", IEEE Transactions on Image Processing, 10(2), pp.266~277 (2001)
- C. Li, "Implicit active contours driven by local binary fitting energy", Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp.1~7 (2007)
- K. Zhang, "Active contours driven by local image fitting energy", Pattern Recognition, 43(4), pp.1199~1206 (2010)
- G. Pan, "Active contour model driven by local entropy energy", Journal of Image and Graphics(China), 18(1), pp.78~85 (2013)
- L. Wang, "Robust level set image segmentation via a local correntropy-based K-means clustering", Pattern Recognition, 47(5), pp.1917~1925 (2014)
- X. Jiang, "Active contours driven by local and global intensity fitting energies based on local entropy", Optik-International Journal for Light and Electron Optics, 126(24), pp.5672~5677 (2015)
- C. Li, "Level set evolution without re-initialization: a new variational formulation", Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp.430~436 (2005)
- C. J. He, "Adaptive distance preserving level set evolution for image segmentation", Journal of Software(China), 19(12), pp.3161~3169 (2008)
- H. Min, "A novel dual minimization based level set method for image segmentation", Neurocomputing, 214, pp.910~926 (2016)
- K. He, "Image segmentation using the level set and improved-variation smoothing", Computer Vision and Image Understanding, 152, pp.29~40 (2016)
- C. Liu, "An improved edge-based level set method combining local regional fitting information for noisy image segmentation", Signal Processing, 130, pp.12~21 (2017)
- S. Zhou, "Correntropy-based level set method for medical image segmentation and bias correction", Neurocomputing, 234, pp.216~229 (2017)
- M. M. Cheng, "Global contrast based salient region detection", IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(3), pp.569~582 (2015)
- S. Sarkar, "Hyper-spectral image segmentation using Rényi entropy based multilevel thresholding aided with differential evolution", Expert Systems with Applications, 50, pp.120~129 (2016)
- C. Tsiotsios, "On the choice of the parameters for anisotropic diffusion in image processing", Pattern Recognition, 46(5), pp.1369~1381 (2013)
- P. Arbelaez, "Contour detection and hierarchical image segmentation", IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(5), pp.898~916 (2011)
- V. Thada, "Comparison of Jaccard, dice, cosine similarity coefficient to find best fitness value for web retrieved documents using genetic algorithm", International Journal of Innovations in Engineering and Technology, 2(4), pp.202~205 (2013)
- D. M. Powers, "Evaluation : from precision, recall and F-measure to ROC, informedness, markedness and correlation", Journal of Machine Learning Technologies, 2(1), pp.37~63 (2011)