Iterative-Back-Projection

利用形态学的腐蚀运算提取图像边界 编写细化算法提取骨架的程序(Using morphology corrosion operation to extract image edgePreparation of thinning algorithm to extract the skeleton program)

LBP（局部二值模式）特征提取，用于人脸识别，(LBP feature extraction for face recognition. . .)

对图像的融合结果进行评价，有均值，标准偏差，熵，梯度，相关系数，光谱扭曲度(Image fusion results of the evaluation, there is the mean, standard deviation, entropy, gradient, correlation coefficient, spectral degree twist)

基于数学形态变换的骨骼CT边缘提取 Canny算子(Transformation of bone based on mathematical morphology edge detection Canny operator CT)

说明：  matlab图像处理用于目标分割，分割后的图像在用形态学滤波处理(matlab image processing for object segmentation, image segmentation using morphological filtering deal)

说明：  利用石川啮合公式编写的程序，适用范围较广，好用，推荐(The program compiled by Ishikawa meshing formula is easy to use and recommended)

几种图像去燥以及图像增强的滤波器示例，中值滤波，陷波滤波器、频域低通滤波，均值滤波等(Several examples of image de dryness and image enhancement filters, median filtering, notch filter, frequency domain low-pass filtering, mean filtering, etc.)

医用图像处理程序,处理dicom文件。开源软件,参考dcmtk(dicom process soft)

RANSAC算法是一种经典的去外点方法，它可以利用特征点集的内在约束关系来去除错误的匹配。(RANSAC algorithm is a classic way to go outside the points, it can take advantage of the inherent characteristics of point sets constraints to remove false matches. )

说明：  当前论文主要考虑的是非信号依赖的高斯噪声下的图像恢复，本程序实现了泊松噪声下的图像恢复，泊松噪声为信号依赖噪声，能够更加有效逼近实际成像系统噪声。( This is the code that was used in the papers "A Nonnnegatively Constrained Convex Programming Method for Image Reconstruction", "Total Variation-Penalized Poisson Likelihood Estimation for Ill-Posed Problems", "Tikhonov Regularized Poisson Likelihood Estimation: Theoretical Justification and a Computational Method", "An Efficient Computational Method for Total Variation with Poisson Negative-Log Likelihood", "An Analysis of Regularization by Diffusion for Ill-Posed Poisson Likelihood Estimation," "An Iterative Method for Edge-Preserving MAP Estimation when Data-Noise is Poisson", and finally, "Regularization Parameter Selection Methods for Ill-Posed Poisson Maximum Likelihood Estimation". See my publications page for more details. The main algorithm is for nonnegatively constrained, regularized Poisson likelihood estimation. At this point you can choose Tikhonov, total variation regularization, and diffusion regularization. A number of other methods are also implemented. Regularizatio)