Variational models provide reliable formulation for segmentation of features and their boundaries in an image, following the seminal work of Mumford-Shah （1989, Commun. Pure Appl. Math.） on dividing a general surfac...Variational models provide reliable formulation for segmentation of features and their boundaries in an image, following the seminal work of Mumford-Shah （1989, Commun. Pure Appl. Math.） on dividing a general surface into piecewise smooth sub-surfaces. A cen- tral idea of models based on this work is to minimize the length of feature＇s boundaries （i.e., 7-I1 Hausdorff measure）. However there exist problems with irregular and oscillatory object boundaries, where minimizing such a length is not appropriate, as noted by Barchiesi et al. （2010, SIAM J. Multiscale Model. Simu.） who proposed to miminize ~：2 Lebesgue measure of the ~,-neighborhood of the boundaries. This paper presents a dual level set selective segmen- tation model based on Barchiesi et al. （2010） to automatically select a local feature instead of all global features. Our model uses two level set functions： a global level set which segments all boundaries, and the local level set which evolves and finds the boundary of the object closest to the geometric constraints. Using real life images with oscillatory boundaries, we show qualitative results demonstrating the effectiveness of the proposed method.展开更多
In this paper we present a selective segmentation model using a dual level set variational formulation.Our variational model aims to segment all objects with one level set function(global)and the selected object,which...In this paper we present a selective segmentation model using a dual level set variational formulation.Our variational model aims to segment all objects with one level set function(global)and the selected object,which is the closest to the geometric constraints(markers),with another level set(local).It is a combination of edge detection,markers distance function and active contour without edges.Experimental results show that our model is more robust than previous work.展开更多
文摘Variational models provide reliable formulation for segmentation of features and their boundaries in an image, following the seminal work of Mumford-Shah （1989, Commun. Pure Appl. Math.） on dividing a general surface into piecewise smooth sub-surfaces. A cen- tral idea of models based on this work is to minimize the length of feature＇s boundaries （i.e., 7-I1 Hausdorff measure）. However there exist problems with irregular and oscillatory object boundaries, where minimizing such a length is not appropriate, as noted by Barchiesi et al. （2010, SIAM J. Multiscale Model. Simu.） who proposed to miminize ~：2 Lebesgue measure of the ~,-neighborhood of the boundaries. This paper presents a dual level set selective segmen- tation model based on Barchiesi et al. （2010） to automatically select a local feature instead of all global features. Our model uses two level set functions： a global level set which segments all boundaries, and the local level set which evolves and finds the boundary of the object closest to the geometric constraints. Using real life images with oscillatory boundaries, we show qualitative results demonstrating the effectiveness of the proposed method.
文摘In this paper we present a selective segmentation model using a dual level set variational formulation.Our variational model aims to segment all objects with one level set function(global)and the selected object,which is the closest to the geometric constraints(markers),with another level set(local).It is a combination of edge detection,markers distance function and active contour without edges.Experimental results show that our model is more robust than previous work.
