matlab

基于深度图像的图像修复算法，首先得到待修复图像的深度图像，然后利用图像的深度图对破损区域进行修复。(Based on the image restoration algorithm of depth image, the depth image of the image to be repaired is obtained, and then the damaged region is repaired by using the depth map of the image.)

不均匀光照下的文字分割，综合运用同态滤波方法、Bernsen局部阈值法、边缘识别法。(Uneven illumination of the text segmentation, integrated use of homomorphic filtering methods, Bernsen local threshold, the edge identification method.)

说明：  360°图像序列的柱面全景拼接算法，matlab仿真程序。(360 image sequence cylindrical panoramic mosaic algorithm, matlab simulation program.)

说明：  用matlab实现的insar处理软件。(Using matlab to achieve the InSAR processing software.)

实现Hough直线检测，标记直线的两端点坐标，再对相似的直线做曲线拟合，可以完美实现边缘连接。(Hough line detection, marking a straight line at both ends of point coordinates, similar linear curve fitting, can be the perfect realization of the edge connector.)

数字图像处理实验-图像处理-含实验题目及源代码(Pseudo-Color Image Processing (a) Implement Fig. 6.23, with the characteristic that you can specify two ranges of gray-level values for the input image and your program will output an RGB image whose pixels have a specified color corresponding to one range of gray levels in the input image, and the remaining pixels in the RGB image have the same shade of gray as they had in the input image. You can limit the input colors to all the colors in Fig. 6.4(a). (b) Download the image in Fig. 1.10(4) and process it with your program so that the river appears yellow and the rest of the pixels are the same shades of gray as in the input image. It is acceptable to have isolated specs in the image that also appear yellow, but these should be kept as few as possible by proper choice of the two gray-level bands that you input into your program.)

基于形态学的滤波算法，tophat算法的matlab源代码即图片(Filtering algorithm based on morphology, tophat algorithm matlab source code for the image)

进行图像格式的转换，可以由bmp转换为tif、jpg等格式(Image format conversion, may tif, jpg bmp format conversion by the)

说明：  利用K均值聚类对图像lena进行分割，图片可根据需求进行更换。(Image Lena is segmented by k-means clustering, and the image can be replaced according to the demand.)

本书第一部分主要介绍机器学习基础，以及如何利用算法进行分类，并逐步介绍了多种经典的监督学习算法，如k近邻算法、朴素贝叶斯算法、Logistic回归算法、支持向量机、AdaBoost集成方法、基于树的回归算法和分类回归树（CART）算法等。第三部分则重点介绍无监督学习及其一些主要算法：k均值聚类算法、Apriori算法、FP-Growth算法。第四部分介绍了机器学习算法的一些附属工具。 全书通过精心编排的实例，切入日常工作任务，摒弃学术化语言，利用高效的可复用Python代码来阐释如何处理统计数据，进行数据分析及可视化。通过各种实例，...( U672C u4E66 u7B2C u4E00 u90E8 u5206 u5206 u4E3B u8991 u4ECB u7ECD u673A u5668 u5B66 u4E60 u57FA u7840 uFF0C u4EE5 u53CA u5982 u4F55 u5229 u7528 u7B97 u6CD5 U8FDB u883C u5206 u7C7B uFF0C u5E76 u9010 u6B65 u4ECB u7ECD u4E86 u591A U90BB u6CD2 u3001 u6734 u7D2 u8D1D u53F6 u65AF u7B97 u6CD5 u3001Logistic u56DE u5F52 u7B97 u6CD5 u3001 u652F u6301 u5411 u91CF u673A u3001AdaBoost u96C6 u6210 U65B9 u6CD5 u3001 u57FA u4E8E u6811 u7R4 u5F52 u7B97 u6CD2 u5286 u5206 u7C7B u56DE u5F52 u6811 uFF08CART uFF09 u7B97 u6CD5 u7B49 u3002 u7B2C u4E09 U90E8 u5209 u5109 u91A u0312 u4E9B u8E1B u8981 u7B97 u6CD5 uFF1Ak u5747 u503C u805A u7B7B U7B97 u6CD3 u30011 by default for uppercase and uupstr U4E9B u9644 u5C5E u5DE5 u5177 u3002 u5168 u4E66 u901A u8FC7 u7CBE U5FC3 u7396 u7392 u7B09 u5B09 u5B09 u5B09 u5B03 U7528 u9184 u5R4 u5904 u7403 u7R03 u7 U5206 u6790 u53CA u53EF u89)