帮助 关于我们

返回检索结果

基于形状保持的CV变分水平集矩形目标分割
Rectangle Object Segmentation Based on Shape Preserving and CV Variational Level Set

查看参考文献12篇

文摘 目标在被局部遮挡、与背景灰度信息相似以及纹理比较明显等情况下, 传统CV模型无法进行准确分割. 为此, 将模型中活动轮廓曲线的水平集函数用先验形状的水平集函数来代替, 使得曲线在演化过程中始终保持某一类特定形状, 从而实现了目标分割过程中的形状保持. 根据形状保持的CV变分水平集分割模型建立适用于矩形目标分割的能量函数模型, 推导出一组Euler-Lagrange常微分方程; 通过水平集函数的不断迭代演化最终实现了矩形目标的分割; 最后演化得到的水平集函数是对矩形目标的定量描述. 3组实验结果证明, 该模型能够解决复杂情况下的矩形目标分割问题, 且具有计算量小、鲁棒性强的优点.
其他语种文摘 CV model is difficult to precisely segment the object which is partially occluded or is similar in gray value with the background or has obvious textures. In this paper, we add shape restraint equations of prior shape to the level set function, which keeps the curve to be a specific class shape during the whole evolvement and realizes shape preserving in object segmentation. Using the proposed model, we built the energy function for rectangle object, deduce a group of corresponding Euler-Lagrange ordinary differential functions, and evolve the level set function. By evolution, rectangle object can be properly segmented, and the final level set function is convicted just the quantitative description of the rectangle object. At the end of the paper, three groups of experimental results validate that the proposed model can correctly segment the rectangle object from complex backgrounds with lessened calculation and strong robustness.
来源 计算机辅助设计与图形学学报 ,2015,27(8):1468-1474 【核心库】
关键词 形状保持 ; CV模型 ; 变分水平集 ; Euler-Lagrange常微分方程 ; 矩形目标分割
地址

中国科学院沈阳自动化研究所光电信息技术研究室, 中国科学院光电信息处理重点实验室, 沈阳, 110016

语种 中文
文献类型 研究性论文
ISSN 1003-9775
学科 自动化技术、计算机技术
基金 国家自然科学基金
文献收藏号 CSCD:5499591

参考文献 共 12 共1页

1.  Otsu N. A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics, SMC,1979,9(1):62-66 被引 2304    
2.  Canny J. A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence,1986,8(6):679-698 被引 1766    
3.  Hao X H. A novel region growing method for segmenting ultrasound images. Proceedings of the IEEE Ultrasonics Symposium,2000:1717-1720 被引 2    
4.  Haralick R M. Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics, SMC,1973,3(6):610-621 被引 862    
5.  Wang D M. Some statistical properties of mathematical morphology. IEEE transactions on Signal Processing,1995,43(8):1955-1965 被引 2    
6.  Mallat S. Singularity detection and processing with wavelets. IEEE Transactions on Information Theory,1992,38(2):617-643 被引 1062    
7.  Udupa J K. Fuzzy connectedness and object definition: theory, algorithms, and applications in image segmentation. Graphical Models and Image Processing,1996,58(3):246-261 被引 57    
8.  Aubert G. Mathematical problems in image processing: partial differential equations and the calculus of variations, 2nd ed,2006:269-273 被引 1    
9.  Chan T F. Active contours without edges. IEEE Transactions on Image Processing,2001,10(2):266-277 被引 1028    
10.  Chan T. Level set based shape prior segmentation. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition,2005:1164-1170 被引 1    
11.  赵骥. 基于自适应帧差和水平集的运动目标检测和分割. 信息与控制,2012,41(2):153-158 被引 8    
12.  李小毛. 基于形状保持主动轮廓模型长直条的检测. 计算机工程,2008,34(1):53-55,58 被引 3    
引证文献 1

1 刘晨 结合全局和局部信息的水平集图像分割方法 计算机应用研究,2017,34(12):3889-3894
被引 2

显示所有1篇文献

论文科学数据集
PlumX Metrics
相关文献

 作者相关
 关键词相关
 参考文献相关

版权所有 ©2008 中国科学院文献情报中心 制作维护:中国科学院文献情报中心
地址:北京中关村北四环西路33号 邮政编码:100190 联系电话:(010)82627496 E-mail:cscd@mail.las.ac.cn 京ICP备05002861号-4 | 京公网安备11010802043238号