xiaobo53

数字图像处理中的sobel算子Matlab程序(Digital image processing in Matlab procedure sobel operator)

用双线性插值法实现的图像缩放，内有详细的使用说明。非常好用(Bilinear interpolation method used to achieve the image scaling, which has detailed instructions. Very easy to use)

说明：  InSAR数据处理研究，其中采用最小二乘和质量图指导两种相位解缠方法(The research of InSAR Data processing)

C# 批量转换图片格式 实例 附完整源码

VTK读取BMP序列图片实现MC算法面绘制，并且将绘制结果保存为STL格式。(VTK read BMP image sequences to achieve MC surface rendering algorithm, and the results will be drawn saved as STL format.)

A MFC demo which can read, write and display images

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，使得水平集函数重新初始化寓于模型的演化之中，分割结果 对参数的依赖性减小。 (This paper mainly study image segmentation of active contour model based on level set. After reviewing the development of the active contour model, curve evolution and level set method are presented, then we proved that the level set method has a stable numerical realization and can describe the topology change of the contour naturally, in addition we introduce level set of variational method. We introduce two different GAC models in this paper: Li Chunming model which is based on gradient information and Chan-Vese model （C-V model） which is based on region information. Based on the drawbacks and advantages of these two models, we improve the C-V model by adding penalizing energy term of signed distance function, at the same time, mending the external energy term which based on region information, then the re-initialization of the level set can accomplish in the model evolution, and the degree of the segmentation result depend on the parameters decline. )

对脑部ct图像的头骨形状进行区域生长算法的运算(Ct images of the brain the skull shape of region growing algorithm for computing)

通过图片的外参数矩阵对图片进行透视变换，对图片的外参数进行校正(camera calibration)

说明：  傅里叶谱与频域低通滤波 (a)从图书网站下载图4.41(a)并计算其(居中)傅里叶谱。(b)显示频谱。(c)在等式(4.8-7)中实现高斯低通滤波器。您必须能够指定结果2D函数的大小，M x N。此外，您必须能够指定高斯函数的中心位置。(d)从图书网站下载图4.41(a)，并对其进行低通滤波，以复制图4.48中的结果。(Fourier Spectrum and Lowpass Filtering in Frequency Domain (a) Download Fig. 4.41(a) from the book web site and compute its (centered) Fourier spectrum. (b) Display the spectrum. (c) Implement the Gaussian lowpass filter in Eq. (4.8-7). You must be able to specify the size, M x N, of the resulting 2D function. In addition, you must be able to specify the location of the center of the Gaussian function. (d) Download Fig. 4.41(a) from the book web site and lowpass filter it to duplicate the results in Fig. 4.48.)