A distribution prior model for airplane segmentation without exact template

doi:10.21629/JSEE.2020.01.07

Journal of Systems Engineering and Electronics ›› 2020, Vol. 31 ›› Issue (1): 56-63.doi: 10.21629/JSEE.2020.01.07

收稿日期:2019-05-13 出版日期:2020-02-20 发布日期:2020-02-25

A distribution prior model for airplane segmentation without exact template

Ming DAI(), Zhiheng ZHOU*(), Yongfan GUO()

School of Electronics and Information Engineering, South China University of Technology, Guangzhou 510640, China

Received:2019-05-13 Online:2020-02-20 Published:2020-02-25
Contact: Zhiheng ZHOU E-mail:dai.ming@mail.scut.edu.cn;zhouzh@scut.edu.cn;guoyonfan@163.com
About author:DAI Ming was born in 1991. He received his B.E. degree in mathematics and applied mathematics, South China University of Technology, Guangzhou, China in 2014. He is currently a Ph.D. student with School of Electronics and Information Engineering, South China University of Technology. His research orientations are image processing and deep learning.E-mail: dai.ming@mail.scut.edu.cn|ZHOU Zhiheng was born in 1977. He received his B.S. and M.S. degrees in Department of Applied Mathematics from South China University of Technology, Guangzhou, China, in 2000 and 2002, respectively. In 2005, he received his Ph.D. degree in School of Electronics and Information Engineering, South China University of Technology. Now he is a professor and his research interests are image processing and image and video transmission. E-mail: zhouzh@scut.edu.cn|GUO Yongfan was born in 1996. He is currently a postgraduate with School of Electronics and Information Engineering, South China University of Technology. His research orientations are image processing and deep learning. E-mail: guoyonfan@163.com
Supported by:
the National Key R & D Program of China(2018YFC0309400);the National Natural Science Foundation of China(61871188);This work was supported by the National Key R & D Program of China (2018YFC0309400) and the National Natural Science Foundation of China (61871188)

摘要/Abstract

Abstract:

In many practical applications of image segmentation problems, employing prior information can greatly improve segmentation results. This paper continues to study one kind of prior information, called prior distribution. Within this research, there is no exact template of the object; instead only several samples are given. The proposed method, called the parametric distribution prior model, extends our previous model by adding the training procedure to learn the prior distribution of the objects. Then this paper establishes the energy function of the active contour model (ACM) with consideration of this parametric form of prior distribution. Therefore, during the process of segmenting, the template can update itself while the contour evolves. Experiments are performed on the airplane data set. Experimental results demonstrate the potential of the proposed method that with the information of prior distribution, the segmentation effect and speed can be both improved efficaciously.

Key words: image segmentation, active contour model (ACM), prior distribution, level set method

. [J]. Journal of Systems Engineering and Electronics, 2020, 31(1): 56-63.

Ming DAI, Zhiheng ZHOU, Yongfan GUO. A distribution prior model for airplane segmentation without exact template[J]. Journal of Systems Engineering and Electronics, 2020, 31(1): 56-63.

图/表 9

参考文献 30

1	NITHILA E E, KUMAR S S. Segmentation of lung from CT using various active contour models. Biomedical Signal Processing and Control, 2019, 47 (1): 57- 62.
2	KHADIDOS A, SANCHEZ V, LI C T. Weighted level set evolution based on local edge Features for medical image segmentation. IEEE Trans. on Image processing, 2017, 26 (4): 1979- 1991.
3	FANG L, QIU T S, ZHAO H Y, et al. A hybrid active contour model based on global and local information for medical image segmentation. Multidimensional Systems and Signal Processing, 2019, 30 (2): 689- 703. doi: 10.1007/s11045-018-0578-0
4	GHADIMI S, ABRISHAMI H, GREBE R, et al. Skull segmentation and reconstruction from newborn CT images using coupled level sets. IEEE Journal of Biomedical and Health Informatics, 2015, 20 (2): 563- 573.
5	KASS M, TERZOPOULUS A, TERZOPOULUS D. Snakes: active contour model. International Journal of Computer Vision, 1988, 1 (4): 321- 331.
6	XU C, PRINCE J L. Snakes, shapes, and gradient vector flow. IEEE Trans. on Image Processing, 1998, 7 (3): 359- 369.
7	CHAN T F, VESE L A. Active contours without edges. IEEE Trans. on Image Processing, 2001, 10 (2): 266- 277.
8	MICHAILOVICH O, RATHI Y, TANNENBAUM A. Image segmentation using active contours driven by the Bhattacharyya gradient flow. IEEE Trans. on Image Processing, 2007, 16 (11): 2787- 2801. doi: 10.1109/TIP.2007.908073
9	WU H, APPIA V, YEZZI A. Numerical conditioning problems and solutions for nonparametric i.i.d. statistical active contours. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2013, 35 (6): 1298- 1311. doi: 10.1109/TPAMI.2012.207
10	ZHANG K, ZHANG L, LAM K. A level set approach to image segmentation with intensity inhomogeneity. IEEE Trans. on Cybernetics, 2016, 46 (2): 546- 557. doi: 10.1109/TCYB.2015.2409119
11	NIU S, CHEN Q, SISTERNES L D, et al. Robust noise region-based active contour model via local similarity factor for image segmentation. Pattern Recognition, 2017, 61 (1): 104- 119.
12	DING K, XIAO L, WENG G. Active contours driven by local pre-fitting energy for fast image segmentation. Pattern Recognition Letters, 2018, 104 (1): 29- 36.
13	WANG L, CHANG Y, WANG H, et al. An active contour model based on local fitted images for image segmentation. Information Sciences, 2017, 418 (1): 61- 73.
14	LIU C, LIU W, XING W. A weighted edge-based level set method based on multi-local statistical information for noisy image segmentation. Journal of Visual Communication and Image Representation, 2019, 59 (1): 89- 107.
15	BRESSON X, VANDERGHEYNST P, THIRAN J P. A variational model for object segmentation using boundary information and shape prior driven by the mumford-shah functional. International Journal of Computer Vision, 2006, 68 (2): 145- 162.
16	CHAN T, ZHU W. Level set based shape prior segmentation. Proc. of the Computer Vision and Pattern Recognition, 2005, 1164- 1170.
17	CREMERS D, ROUSSON M, DERICHE R. A review of statistical approaches to level set segmentation: integrating color, texture, motion and shape. International Journal of Computer Vision, 2007, 72 (2): 195- 215.
18	FOULONNEAU A, CHARBONNIER P, HEITZ F. Affine-invariant geometric shape priors for region-based active contours. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2006, 28 (8): 1352- 1357. doi: 10.1109/TPAMI.2006.154
19	LEVENTON M E, FAUGERAS O, GRIMSON W E L, et al. Level set based segmentation with intensity and curvature priors. Proc. of the IEEE Workshop on Mathematical Methods in Biomedical Image Analysis, 2000, 4- 11.
20	LEVENTON M E, GRIMSON W E L, FAUGERAS O. Statistical shape influence in geodesic active contours. Proc. of the Computer Vision and Pattern Recognition, 2000, 316- 323.
21	ZHOU Z, DAI M, ZHONG H. Parametric shape prior model used in image segmentation. Journal of Systems Engineering and Electronics, 2016, 27 (5): 1115- 1121. doi: 10.21629/JSEE.2016.05.19
22	CHENG M M, MITRA N J, HUANG X, et al. Salient shape: group saliency in image collections. Visual Computer, 2014, 30 (4): 443- 453. doi: 10.1007/s00371-013-0867-4
23	KIM J, FISHER J W, YEZZI A, et al. A nonparametric statistical method for image segmentation using information theory and curve evolution. IEEE Trans. on Image Processing, 2005, 14 (10): 1486- 1502. doi: 10.1109/TIP.2005.854442
24	GONG M, LI H, ZHANG X, et al. Nonparametric statistical active contour based on inclusion degree of fuzzy sets. IEEE Trans. on Fuzzy Systems, 2016, 24 (5): 1176- 1192. doi: 10.1109/TFUZZ.2015.2505328
25	NIU Y, CAO J, ZHOU Z. An adaptive stopping active contour model for image segmentation. Journal of Electrical Engineering and Technology, 2019, 14 (1): 445- 453. doi: 10.1007/s42835-018-00030-8
26	HANBAY K, TALU M F. A novel active contour model for medical images via the Hessian matrix and eigenvalues. Computers and Mathematics with Applications, 2018, 75 (9): 3081- 3104. doi: 10.1016/j.camwa.2018.01.033
27	HSIEH C W, CHEN C Y. An adaptive level set method for improving image segmentation. Multimedia Tools and Applications, 2018, 77 (15): 20087- 20102. doi: 10.1007/s11042-017-5434-y
28	SOOMRO S, MUNIR A, CHOI K N. Fuzzy c-means clustering based active contour model driven by edge scaled region information. Expert Systems with Applications, 2019, 120 (1): 387- 396.
29	LI C, XU C, GUI C, et al. Level set evolution without reinitialization: a new variational formulation. Proc. of the Computer Vision and Pattern Recognition, 2005, 430- 436.
30	MITICHE A, AYED I B. Variational and level set methods in image segmentation. Berlin: Springer Science & Business Media, 2010.

Algorithm	Precision/%	Recall/%	FM/%	Time/s
Bhat.	58.34	72.75	57.36	145.57
AMP	58.96	72.38	58.98	80.65
Proposed	74.32	73.43	74.26	65.64

A distribution prior model for airplane segmentation without exact template

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

图/表 9

参考文献 30

相关文章 0

编辑推荐

Metrics

本文评价