0502：图像的欧拉数计算，适合初学者…

摄像机图像序列的全景图拼接，希望能有些用处-Camera panorama image stitching sequence, hoping to be useful

图象任意角度的旋转、水平、垂直镜像、缩放、裁剪等算法的实现-arbitrary image rotation angle, horizontal, vertical image, zoom, cutting algorithm implementation

图像分割 图像处理的 一本很重要 很实用的书-Image segmentation image processing is a very important and practical book

MultiScale Retinex 色彩恒常性的彩色图像增强算法-Color constancy MultiScale Retinex for Color Image Enhancement Algorithm

用于各种图像分割的算法 ，隐痛上传，从上面的边缘检测，二值化，区域分割一些经典的算法来加深对图像分割的理解。粗略分析了这些算法的优点很不足。对于课题算法的选取起了很大的帮助-for various image segmentation algorithm secret anguish uploads from above the edge detection, two values, Segmentation some classical algorithm for image segmentation deepen understanding. Rough analysis of the advantages of these algorithms are inadequate. Algorithm for the selection of topics has been most helpful

HOUGHTRANSFORM uses Hough transform for line detection. (very fast and easy solution).

基于radon变换的SR-CT断层重建程序，matlab编写-Radon transform based on the SR-CT tomography reconstruction procedures, matlab prepared

《VC++数字图像处理》源代码 何斌著，很好的学习数字图像处理的资料-" VC++ digital image processing," source code

指纹图像的增强,细化.指纹特征点的提取,单个特征点的对比,单个特征星的对比,指纹图像的对比-fingerprint image enhancement and refinement. Fingerprint Feature Extraction Point, a single point of contrast, Characteristics of single-contrast images of fingerprints

实现简单图像处理，包括256色转灰度图、Hough变换、Walsh变换、中值滤波、二值化变换、亮度增减、傅立叶变换、反色、取对数、取指数、图像平移、图像旋转、图像细化、图像缩放、图像镜像、均值滤波、对比度拉伸、拉普拉斯锐化（边缘检测）、方块编码、梯度锐化、灰度均衡、直方图均衡、离散余弦变换、维纳滤波处理、逆滤波处理、阈值变换、高斯平滑。-simple image processing, including color to grayscale 256, the Hough transform, Walsh transform, Median filtering, binary conversion, brightness changes, Fourier transform, and color-from the few to admit index, image shifting, image rotation, refined images, image scaling, image mirroring, mean filtering, contrast stretching, Laplacian sharpening (edge detection), block codes, Sharpening gradient, gray balance, histogram equalization, discrete cosine transform, Wiener filter, reverse filtering, threshold transform, Gaussian smoothing.