搜索资源列表
tu_xiang_jianche
- 数字图像的边缘检测是图像分割、目标区域的识别、区域形状提取等图像分析领域十分重要的基础，图像理解和分析的第一步往往就是边缘检测，目前它以成为机器视觉研究领域最活跃的课题之一，在工程应用中占有十分重要的地位。本文向读者简单介绍一下这个技术，并给出了在Visual C++环境下实现的代码。-Digital Image Edge detection is image segmentation, target recognition in the region, regional shape extra
level_set_region
- active contour算法中的经典算法：基于region的levle set算法文献
regionbased_seg_demo
- This code implements the well-known Chan-Vese segmentation algorithm from the paper \"Active Contours Without Edges.\" This technique deforms an initial curve so that it separates foreground from background based on the means of the two regions.
segmentation
- Region based Active Contour的Matlab实现代码。很经典的方法-Region based Active Contour of Matlab code. Classic method
LocalizedRegionBasedActiveContours
- region based active contours
regionbased_seg
- Region Based Active Contour Segmentation：基于活动轮廓的图像分割算法，ＭＡＴＬＡＢ代码，可用于目标提取，包括实验图片-Region Based Active Contour Segmentation: Active Contour-based image segmentation algorithm, MATLAB code, and can be used for object extraction, including the experimental
RdfSnap123_0430_Setup
- 一款完全免费的专业级屏幕捕捉软件，能够让您得心应手地捕捉到需要的屏幕截图。捕捉图像方式灵活，主要可以捕捉整个屏幕、活动窗口、选定区域、固定区域、选定控件、选定菜单、选定网页等，图像输出方式多样，主要包括文件、剪贴板、画图和打印机。软件具有捕捉光标、设置捕捉前延时、显示屏幕放大镜、自定义捕捉热键、图像文件自动按时间或模板命名、捕捉成功声音提示、重复最后捕捉、预览捕捉图片、图像打印、图像裁切、图像去色、图像反色、图像翻转、图像旋转、图像大小设置、常用图片编辑、墙纸设置等功能。捕捉到的图像能够以保存图
RegionBasedSnake
- 基于区域的主动轮廓模型算法，是论文《Active Contours Without Edges》的VC算法实现-Region-based active contour model algorithm, for the paper " Active Contours Without Edges" of the VC algorithm
Image_Segmentation_Active_Coutour_Local_Binary_Fit
- 李纯明最新实现的局部活动轮廓模型的图像分割，比CV模型方法好很多。包含所实现的论文Minimization of Region-Scalable Fitting Energy for Image Segmentation-Li Chunming latest realization of the local active contour model for image segmentation, much better than the CV model approach. The paper
A-New-Mura-Defect-Method
- mura检测的论文。First, a set of real Gabor filters are applied to eliminating the global textured backgrounds. Then, the level set method is employed for image segmentation with a new region-based active contours model, which is an improvement of t
Image-Segmentation-Method
- 本文主要研究基于水平集方法的活动轮廓模型图像分割，在回顾活动轮廓模 型发展的基础上，介绍了曲线演化理论及其水平集方法，证明了水平集方法可以 有稳定的数值实现方式且其处理拓扑变化的自然性，进一步引出了变分水平集方 法。 本文介绍了两种不同的几何活动轮廓模型：基于梯度信息的李纯明模型以及 基于区域信息的 Chan-Vese 模型（C-V 模型）。在分析上述两模型的优缺点上，提 出了一种改进的 C-V 模型，改进模型引入了距离约束项，同时对基于区域的外部 能量项进行了改进，
RSF-Active-contour-model.
- 实现论文 Minimization of region-scalable fitting energy for image segmentation 中提出的RSF模型，也适用于LBF模型，用于图像分割。-RSF model of the papers Minimization of region-scalable fitting energy for image The a segmentation , LBF model for image segmentation.
GVD
- Active Contour (or Snake) Model is recognized as one of the efficient tools for 2D/3D image segmentation. How- ever, traditional snake models prove to be limited in sev- eral aspects. The presentpaper describes a set of difision equations applied ro
LBF
- ：LBF 模型是一个著名的基于区域的活动轮廓模型。与PC（Piecewise Constant）模型不同，该模型引入了一个以高斯函数为 核函数的局部二值拟合（Local Binary Fitting，LBF）能量。因为LBF 能量能够获取图像的局部信息，所以LBF 模型解决了PC 模型 不能处理灰度不均一图像的分割问题。提出了一个改进的LBF 模型，它使用一个新的核函数代替高斯核函数-: LBF model is a well-known region-based active cont
classical-image-segmentation-
- 图像处理中经典的C-V（无边缘活动轮廓）模型和GAR（测地线活动区域）模型！- classic CV ( active contour without edge ) model and GAR (geodesic active region) model in Image processing!
Chebyshev
- 无边活动轮廓模型(C-V模型)是水平集分割方法中的一种经典模型.传统的无边活动轮廓模型将灰度同质作为区域分割准则,这使其对于仅含两个同质区域且灰度变化不大的图像能够取得很好的分割效果,但对灰度渐变图像分割时,该模型往往无法得到正确结果.本文针对这一问题,通过引入Chebyshev距离构造一种新的相似度,以此来表征演化曲线内外灰度差异,修改了传统无边活动轮廓模型中均值取值的定义,使得演化曲线在图像灰度变化缓慢区域获得较大的驱动力.新模型克服了传统无边活动轮廓模型不能正确分割灰度渐变图像的不足.实验
Chan-Vese-model
- Chan-Vese模型是基于区域的几何活动轮廓模型,此模型根据灰度级的同质性进行分类，图像域包括背景区域与前景区域。-Chan-Vese geometric model is region-based active contour model, this model are classified according to the gray level of homogeneity, including the background image field area and the foregro
Tper prentod
- This paper presents a novel active contour model in a variational level set formulation for simultaneous segmentation and bias field estimation of medical images. An energy function is formulated based on improved Kullback-Leibler distance (KLD) wi
postiasser
- this is the first variational image segmentation algorithm that involves region-dependent multi-dimensional descr iptors based on the optimal transport theory. The distributions are represented owing to non-parametric kernel density estimators(In thi