重庆网站制作珠海公司,技术提供微信网站开发,网站推广怎么做与发布,深圳 网站#x1f4a5;#x1f4a5;#x1f49e;#x1f49e;欢迎来到本博客❤️❤️#x1f4a5;#x1f4a5; #x1f3c6;博主优势#xff1a;#x1f31e;#x1f31e;#x1f31e;博客内容尽量做到思维缜密#xff0c;逻辑清晰#xff0c;为了方便读者。 ⛳️座右铭欢迎来到本博客❤️❤️ 博主优势博客内容尽量做到思维缜密逻辑清晰为了方便读者。 ⛳️座右铭行百里者半于九十。 本文目录如下 目录 1 概述 2 运行结果 3 参考文献 4 Matlab代码实现 1 概述
本代码实现参考了以下三篇文章。 [1] S. Lankton and A.Tannenbaum.Localizing Region-Based Active Contours. IEEE Trans on Image Proceesing 2008. [2] A Yezzi Jr, A Tsai, A Willsky. A statistical approach to snakes for bimodal and trimodal imagery,IEEE ICCV 1999. [3] Chan, T. F., Vese, L. A. Active contours without edges. IEEE Transactions on Image Processing, 2001. 使用[1]设定的框架实现了Mean Separation模型[2]和Chan-Vese模型[3]的本地化版本。
[1] S. Lankton和A.Tannenbaum在2008年发表的文章《Localizing Region-Based Active Contours》。该研究提出了一种基于区域的主动轮廓局部化方法为本文的实现框架提供了重要的参考。
[2] A Yezzi Jr, A Tsai, A Willsky在1999年的文章《A statistical approach to snakes for bimodal and trimodal imagery》。这篇文章介绍了一种基于统计方法的蛇算法用于处理双模态和三模态图像。本文在实现Mean Separation模型时借鉴了该方法并进行了本地化的改进。
[3] Chan, T. F., Vese, L. A.在2001年发表的文章《Active contours without edges》。该研究提出了一种无边缘的主动轮廓方法能够有效地处理图像中缺乏明显边缘的情况。本文在实现Chan-Vese模型时参考了该方法并进行了相应的本地化调整。
基于[1]设定的框架本文成功实现了Mean Separation模型和Chan-Vese模型的本地化版本。通过结合这些先前的研究成果我们能够有效地处理各种图像并获得准确的分割结果。这些模型的实现为图像处理领域的研究和应用提供了有力的工具和方法。未来的工作可以进一步优化和扩展这些模型以适应更广泛的应用场景。
2 运行结果 部分代码
function seg local_AC_MS(Img,mask_init,rad,alpha,num_it,epsilon) % This function aims to implement Shawn Lanktons local active contour. And % the energy model is the MS model as defined in eq.(15)-(19). % The local variables are calculated by filtering operation instead of % iterating inspired by Chunming Lis IEEE TIP 2008 paper % % One small change is that I used a square window instead of disk for % localization % % Input: % 1. Img: image needs to be segmented % 2. mask_init: intialization represented by binary image % 3. rad: the side length of the square window % 4. alpha: the coeficicent to balance the image fidality term and the % curvature regularization term % 5. num_it: maximum number of iterations % 6. epsilon: epsilon used for delta and heaviside function % Created by Jincheng Pang, Tufts University 11/09/2012
phi0 mask2phi(mask_init); phi phi0;
B0 ones(2*rad1,2*rad1); % B0 fspecial(disk,rad);
KIconv2(Img,B0,same); KONEconv2(ones(size(Img)),B0,same); for ii 1:num_it mask Heaviside2(phi,epsilon);
IImg.*mask; temp1conv2(mask,B0,same); temp2conv2(I,B0,same); c1temp2./(temp1); % local mean value inside c2(KI-temp2)./(KONE-temp1); % local mean value outside
A1 temp1; A2 conv2(1-mask,B0,same); %%%%% D (A1.*A2eps); term1 (A2-A1)./D; term2 (A2.*c1.^2-A1.*c2.^2)./D; term3 (A2.*c1-A1.*c2)./D; dataForce conv2(term1.*Dirac2(phi,epsilon),B0,same).*Img.*Img conv2(term2.*Dirac2(phi,epsilon),B0,same)-2.*Img.*conv2(term3.*Dirac2(phi,epsilon),B0,same); %%% During the implementation, Img should be separated out of the filtering operation!!! % dataForce conv2(term1.*Dirac2(phi,epsilon).*Img.^2,B0,same) conv2(term2.*Dirac2(phi,epsilon),B0,same)-2.*conv2(term3.*Dirac2(phi,epsilon).*Img,B0,same); dataForce dataForce/max(abs(dataForce(:))); % % curvature get_curvature1(phi); curvature curvature_central(phi); dphi Dirac2(phi,epsilon).*(-dataForce alpha*curvature);
dt .48/(max(abs(dphi(:)))eps);
%-- evolve the curve phi phi dt.*dphi;
%-- Keep SDF smooth phi sussman(phi, .5); if(mod(ii,10) 0) showCurveAndPhi(Img,phi,ii); end end
seg (phi0);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%Auxiliary functions %%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %-- level set re-initialization by the sussman method function D sussman(D, dt) % forward/backward differences a D - shiftR(D); % backward b shiftL(D) - D; % forward c D - shiftD(D); % backward d shiftU(D) - D; % forward
3 参考文献 文章中一些内容引自网络会注明出处或引用为参考文献难免有未尽之处如有不妥请随时联系删除。 [1] S. Lankton and A.Tannenbaum.Localizing Region-Based Active Contours. IEEE Trans on Image Proceesing 2008.
[2] A Yezzi Jr, A Tsai, A Willsky. A statistical approach to snakes for bimodal and trimodal imagery,IEEE ICCV 1999.
[3] Chan, T. F., Vese, L. A. Active contours without edges. IEEE Transactions on Image Processing, 2001.
4 Matlab代码实现
