引入差分图像具有多重特性的图像分割模型  

Novel Kind of Image Segmentation Model Introducing Difference Image with Multiple Segmentation Characters

作　　者：何玲娜[1,2] 曹建伐 郑河荣[1,2] 

机构地区：[1]浙江工业大学计算机科学与技术学院,杭州310023 [2]浙江省可视媒体智能处理技术研究重点实验室,杭州310023

出　　处：《计算机科学》2015年第6期303-307,共5页Computer Science

基　　金：浙江省自然科学基金资助项目(LY12F02035)资助

摘　　要：大多数经典活动轮廓模型只具有某些方面的优势,不能同时满足处理复杂图像的要求,对此提出一种具有多重分割特性的分割模型。模型通过引入差分图像,将差分图像的BGFRLS模型作为全局控制项,以保证模型能够最大限度地检测到所有的目标边缘;其次,将长度项设为局部项,使得分割进一步精确化,并将Li方法中的惩罚项加入到模型中,避免了重新初始化水平集函数,提高了分割效率;最后,模型在全局控制项和局部控制项之间引入了自适应权值,避免了过多的参数设置。通过上述方法使得模型具有如下优点:1)具有更强的全局分割性;2)可以分割灰度不均匀的图像,而且能够有效地检测出虚弱目标边缘;3)算法具有一定鲁棒性,能够克服一定噪声。实验表明,该模型在保证分割效率的前提下可以分割灰度不均匀的图像,而且能够有效检测出虚弱目标边缘,此外还具有更强的全局分割性,并能抵御一定噪声。Most of classical active contour models only have advantages on some ways, however they can＇t deal with complex images. So the paper proposed a kind of segmentation model with multiple characters. This paper introduced difference image and took the BGFRLS model of difference image as global control of model. In addition, to avoid re-ini- tialization of level set function and shorten the computational time, this paper introduced the penalization function in Li method. Furthermore, to decrease regulation parameters, the self-adaption weight between global control term and local control term was used in place of constant weight. Through these improvements, our method has some advantages as follows. First, the method has the global segmentation character. Second, by means of introducing the difference image, our method is able to process the image with intensity inhomogeneity and detect the weak edge. Third, ours model is ro- bust to image noise. Ours experiments demonstrate that the proposed method is indeed able to segment the images with intensity inhomogeneity,and is able to detect the weak edge. In addition, it has global segmentation character and robustness.

关 键 词：图像分割 差分图像 灰度不均匀 全局分割 鲁棒性 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]