基于隐马尔科夫随机场邻域选择的细节保护图像分割

doi:10.3969/j.issn.1006-2475.2017.10.004

计算机与现代化

基于隐马尔科夫随机场邻域选择的细节保护图像分割

1.西安电子科技大学附中,陕西西安710071；2.西安电子科技大学电子工程学院，陕西西安710071

收稿日期:2017-03-10 出版日期:2017-10-30 发布日期:2017-10-31
作者简介:田沁怡(2000-),女,陕西西安人,西安电子科技大学附中学生，研究方向：图像处理；田小林（1972-），男，陕西西安人，西安电子科技大学电子工程学院副教授，研究方向：图像处理。

Image Segmentation with Detail Preserving Based on HMRF Using Neighborhood Selection

1. High School Attached to Xidian University, Xi’an 710071, China;  2. School of Electronic Engineering, Xidian University, Xi’an 710071, China

Received:2017-03-10 Online:2017-10-30 Published:2017-10-31

摘要/Abstract

摘要： 由于马尔科夫随机场（Markov Random Fields，MRF）区域标识模型的滤波效应，在合成孔径雷达（SAR）图像处理过程中，细节结构会被部分保留或者完全丢失。本文提出一种基于散射描述子的自适应邻域系统隐MRF（Hidden MRF，HMRF）图像分割方法，以实现更好地保留图像细节特征和边缘区域，从而改善图像的分割效果。为了提高可靠性和自适应性，将模糊c均值（Fuzzy c-means，FCM）聚类算法与散射变换相结合，实现邻域形状的自适应选择。从不同的邻域形状中，选择具有最高模糊隶属度的邻域形状进行HMRF区域标识过程。实验结果表明，相比较于一般HMRF使用固定形状的邻域系统，本文所提出的算法改善了分割效果，特别是图像细节结构信息得到了很好的保护。

关键词: 图像分割, 隐MRF模型, 散射描述子, 邻域选择, 模糊c均值

Abstract: Because of the filtering effect of the Markov random fields (MRF) region label model, detail structures may be preserved partially or lost entirely for synthetic aperture radar (SAR) image. The hidden MRF (HMRF) image segmentation approach with adaptive neighborhood systems based on scattering descriptor is proposed to better preserve detail features and border areas and to improve the segmentation effect. In order to improve the reliability and the adaptivity, fuzzy c-means (FCM) clustering algorithm is incorporated into scattering transform, where the choice of neighborhood shapes can be implemented adaptively. From among the different shape alternatives, the one with the highest fuzzy membership is chosen to compute the HMRF region label process. Experiment results demonstrate that the proposed algorithm improves the segmentation effect over the conventional HMRF using fixed shapes of neighborhood systems while detail structures are preserved well.

Key words: image segmentation, hidden Markov random fields model, scattering descriptor, neighbourhood selection, fuzzy c-means

田沁怡 1，田小林 2. 基于隐马尔科夫随机场邻域选择的细节保护图像分割[J]. 计算机与现代化, doi: 10.3969/j.issn.1006-2475.2017.10.004.

TIAN Qin-yi 1, TIAN Xiao-lin 2. Image Segmentation with Detail Preserving Based on HMRF Using Neighborhood Selection[J]. Computer and Modernization, doi: 10.3969/j.issn.1006-2475.2017.10.004.

参考文献

［1］ Cross G R, Jain A K. Markov random field texture models［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1983,5(1):25-39.

［2］ Derin H, Elliott H. Modeling and segmentation of noisy and textured images using Gibbs random fields［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1987,9(1):39-55.

［3］ Deng Huawu, Clausi D A. Unsupervised segmentation of synthetic aperture radar sea ice imagery using a novel Markov random field model［J］. IEEE Transactions on Geoscience and Remote Sensing, 2005,43(3):528-538.

［4］ Ghamisi P, Benediktsson J A, Ulfarsson M O. Spectral-spatial classification of hyperspectral images based on hidden Markov random fields［J］. IEEE Transactions on Geoscience and Remote Sensing, 2014,52(5):2565-2574.

［5］ Desrosiers C. An unsupervised random walk approach for the segmentation of brain MRI［C］// Proceedings of IEEE International Conference on Biomedical Imaging. 2014:337-340.

［6］ Basak K, Dey G, Sheet D, et al. Probabilistic graphical modeling of speckle statistics in laser speckle contrast imaging for noninvasive and label-free retinal angiography［C］// Proceedings of the 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 2015:6244-6247.

［7］ Smits P C, Dellepiane S G. Synthetic aperture radar image segmentation by a detail preserving Markov random field approach［J］. IEEE Transactions on Geoscience and Remote Sensing, 1997,35(4):844-857.

［8］ Ghamisi P, Benediktsson J A, Ulfarsson M O. Spectral-spatial classification of hyperspectral images based on hidden Markov random fields［J］. IEEE Transactions on Geoscience and Remote Sensing, 2014,52(5):2565-2574.

［9］ Olding W C, Olivier J C, Salmon B P. A Markov random field model for decision level fusion of multi-source image segments［C］// Proceedings of 2015 IEEE International Geoscience and Remote Sensing Symposium. 2015:2385-2388. 

［10］Yang Xi, Gao Xinbo, Tao Dacheng, et al. An efficient MRF embedded level set method for image segmentation［J］. IEEE Transactions on Image Processing, 2015,24(1):9-21.

［11］Lowe D G. Distinctive image features from scale-invariant keypoints［J］. International Journal of Computer Vision, 2004,60(2):91-110.

［12］Bruna J, Mallat S. Classification with scattering operators［C］// Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2011:1561-1566.

［13］Bruna J, Mallat S. Invariant scattering convolution networks［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013,35(8):1872-1886.

［14］Mallat S. Group invariant scattering［J］. Communications on Pure and Applied Mathematics, 2012,65(10):1331-1398.

［15］Bruna J. Scattering representations for pattern and texture recognition［D］. PhD thesis, CMAP, Ecole Polytechnique, 2012.

［16］Alan B. Handbook of Video and Image Processing［M］. 2nd edition, Academic Press, 2005.

［17］ Shen Shan, Sandham W, Granat M, et al. MRI fuzzy segmentation of brain tissue using neighborhood attraction with neural-network optimization［J］. IEEE Transactions on Information Technology in Biomedicine, 2005,9(3):459-467.

基于隐马尔科夫随机场邻域选择的细节保护图像分割

Image Segmentation with Detail Preserving Based on HMRF Using Neighborhood Selection

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 0

编辑推荐

Metrics

本文评价