matlab 处理

文章讲述了一种基于多边形的优化排样方法，结合不规则件特征确定了零件排放的最佳位置，给出了排料规则库的设计，通过排料空间的边界组织和边界更新，形成了新的排样空间。 (Article describes a polygon-based layout optimization method combined with irregular parts on characters of the part the best place to discharge given row of the design rule base material, the border by nesting space organization and boundary updates, formed The new layout space.)

基于FCM和水平集的图像分割，以脑颅图像为例，分割结果良好(FCM and the level set based image segmentation to skull image, for example, good segmentation results)

用matlab实现散焦模糊图像的参数估计，并运用Lucy法复原图像，效果不错(Using matlab realize defocus blurred image parameter estimation, and the use of Lucy France restored image, good results)

基于spline的图像感兴趣区域提取，可用于基于区域的图像检索系统中。(spline- based region of interest image extraction, can be used for regional image retrieval system.)

说明：  基于Kmeans算法的图像分割，一般Kmeans是数据挖掘中用来聚类的，本试验利用图像中的灰度值作为Kmeans算法的原始点，进行图像分割(Kmeans algorithm based on image segmentation, data mining in general Kmeans is used to clustering, the trial use of the image gray value as the original algorithm Kmeans spots for Image Segmentation)

说明：  图像四叉树分解，对于图像灰度值进行四叉树分解，并覆盖原图显示(Image quadtree decomposition)

这个程序是马尔可夫的图像分割程序 对于初学者学习马尔可夫分割图像有帮助(This program is a Markov image segmentation program for beginners to learn Markov image segmentation helpful)

说明：  图像处理算法质量评估，图像结构相似度（ssim)评价的matlab实现，并附带原始论文pdf(This is an implementation of the algorithm for calculating the Structural SIMilarity (SSIM) index between two images. Please refer to the following paper: Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, "Image quality assessment: From error measurement to structural similarity" IEEE Transactios on Image Processing, vol. 13, no. 1, Jan. 2004.)

模糊聚类图像分割，还包括spectral clustering及其改进版（按照pr2010某paper）(image segmentation using fuzzy c means & spectral clustering)

1、读取显示所给PET图像（SE2IM210）。 2、学习imhist函数，画出所给PET图像的直方图。 3、根据直方图为该PET图像选择合适的显示窗（imshow参数），使胸 部轮廓等解剖结构可见。 4、根据直方图选择合适阈值，分割肿瘤区域与正常区域，计算肿瘤区 域内图像灰度均值、方差、最大值及最小值。(1 give PET images displayed, read (SE2 IM210). 2, learning, imhist function, draw the give PET image histogram. 3, according to the histogram for the PET image to choose the appropriate display window (imshow parameters), make bosom Outline of anatomical structures such as visible. 4, according to the histogram, choose appropriate threshold segmentation tumor area and normal area, calculating the tumor area In the domain of image grayscale average and variance, the maximum and minimum values.)