color-image-seg

对图像进行GMM（混合高斯）拟合后用EM算法进行分割(Image GMM (Gaussian Mixture) after fitting algorithm using EM Segmentation)

这是一个去背景并且进行阈值分割的程序，阈值的选择采用迭代法(This is the background to a threshold value and segmentation procedures, the threshold value of the options using the iterative method)

图片文字 合成实例

分数阶B样条小波用于图像去噪的matlab程序。(B fractional spline wavelet for image denoising matlab program.)

image segmentation with ICM algoritm

说明：  对一副已经给出的图像进行中值滤波处理，利用罗伯特算子、索伯尔算子、canny算子、拉普拉斯算子，分别进行边缘检测。 具体见说明文档。(A given image is processed by median filter. Robert operator, Sobel operator, Canny operator and Laplace operator are used for edge detection. See the instruction document for details.)

说明：  对图像提取同名点，并根据同名点建立delaunay三角网，后续可进行图像配准，或根据三角网进行控制点筛选(The same name points are extracted from the image, and the Delaunay triangulation network is established according to the same name points. The image registration can be carried out later, or the control points can be selected according to the triangulation network)

形态学pathopenings的实现，对线特征提取等图像处理有令人惊奇的效果。(Morphological pathopenings the realization of the line feature extraction and other image processing have surprising effects.)

此文档中的代码可以计算5种图像的质量参数，分别是：图像灰度均值、图像方差、图像平均梯度、熵和峰值信噪比及均方误差。(The code in this document can be calculated five kinds of image quality parameters, namely: the image gray value, image variance, average gradient image, entropy and peak signal to noise ratio and mean square error.)

混合高斯(Mixture of Gaussian, MOG) 背景建模算法和Codebook 背景建模算法被广泛应用于监控视频的运动目标检测问题, 但混合高斯的球体模型通常假设RGB 三个分量是独立的, Codebook 的圆柱体模型假设背景像素值在圆柱体内均匀分布且背景亮度值变化方向指向坐标原点, 这些假设使得模型对背景的描述能力下降. 本文提出了一种椭球体背景模型, 该模型克服了混合高斯球体模型和Codebook 圆柱体模型假设的局限性, 同时利用主成分分析(Principal components analysis, PCA) 方法来刻画椭球体背景模型, 提出了一种基于主成分分析的Codebook 背景建模算法. 实验表明, 本文算法不仅能够更准确地描述背景像素值在RGB 空间中的分布特征, 而且具有良好的鲁棒性.(The background modeling algorithm of mixture of Gaussian (MOG) and codebook is widely used in moving object detection of surveillance video. However, the ball model of MOG usually assumes that the three components of RGB are independent, while the cylinder model of codebook assumes that the value of background pixel is distributed uniformly within the cylinder and the changing direction of brightness points to the origin of the coordinate system. These assumptions reduce the descriptive capability for background modeling. Therefore, the paper proposes an ellipsoid- based background model, which overcomes the MOG and codebook0s limitations. By using principal component analysis to depict the ellipsoid background model, a novel PCA-based codebook background modeling algorithm is proposed. Experiments show that this algorithm can not only give more accurate description of the distribution of background pixels but also have a better robustness.)