资源列表
位图BMP格式图像文件处理类源码VC版
- 该类集成了常用的BMP位图文件的一些经常用到的比如文件读写等操作-such integrated common bitmap BMP documents some of the frequently used documents such as literacy, etc
边缘密度、信噪比、重复模式的计算
- 该类集成了图像的边缘密度，以及重复计算分析等前期处理方法，为图像后续处理做了很好的铺垫-such integration on the verge of image density and the calculated analysis of pre-processing methods, for image-processing done a very good bedding
自编的画图程序
- 能够绘制简单的几何图形.如圆,椭圆,正方形,矩形,任意线等.-to drawing simple geometric shapes. If a round, oval, square, rectangular, arbitrary lines.
ColorSpace_src
- 图像颜色空间的转换.RGB -HSV. -image color space conversion. RGB-HSV.
etv-beta-6.tar
- etv the personal video recorder-ETV the personal video recorder
AFMM_Inpainting
- 基于快进算法的破损图像修复算法，只要将图像中的破损区域由纯红色标出，该算法即可自动完成对该区域的修复。要求修复图像格式为BMP格式。-algorithm based on fast forward damaged the image restoration algorithms, as long as the image of the region damaged by the pure red mark, The algorithm can automatically complete res
Inpainting
- 采用M.Bertalmio and G.Sapiro论文中算法的图像修复C++代码。需要提供原始破损图像和破损区域的掩模图像，修复结果实时显示。-used Bertalmio and G. M. Sapiro papers algorithm for image restoration generation C yards. The need for the original image and damaged the damaged region mask images, results, r
RBFInpainting
- 采用径向基函数进行图像修复的VC++源代码，用户可交互式地选择破损修复区域。-RBF used for image restoration VC source code, users can interactively choose to repair damaged region.
Mumford
- 采用水平集（level set）方法和mumford-shah图像分割模型的图像分割matlab源代码。-horizontal Collection (level set) methods and Mumford - shah Segmentation Model Image segmentation Matlab source code.
matlab_toolbox_fast_marching
- 水平集（level set）中快进算法（fast marching）算法的matlab工具箱，内容很全面。-Level Set (level set), Fast Forward algorithm (fast marching) algorithm m atlab Toolbox, which is very comprehensive.
matcher
- 自己写的两副图像的特征点匹配，其中用了opencv类库，功能包括：特征点的亚像素精度检测，特征点初始匹配，基本矩阵计算及对极约束引导匹配等。-himself wrote the two images and matching feature points, which used the OpenCV library, features include : the feature points subpixel accuracy testing, the initial matching feat
bitmap_picture_src
- 实现2D图像的边缘检测功能,希望大家喜欢-2D images to achieve the edge detection function hope you like