YUV_tool

一种新的基于snake模型的水平集图像分割方法：用matlab编程实现，可进行灰度图像的分割，里面有演示实例，便于学习。(A new snake model based on the level set image segmentation methods: using matlab programming implementation, can be gray-scale image segmentation, there are demonstration examples for study.)

代码主要用来模拟林芝机场附近一天内4个不同时间GDOP变化情况(Code is mainly used to simulate the Nyingchi Airport near four different times within a day GDOP changes)

利用DCT字典对图形进行稀疏表示，实现两幅图像的融合(Using the DCT dictionary to sparse representation of the graphics to achieve the fusion of two images)

本程序主要实现暗背景下含高斯噪声的光斑滤除噪声，实现光斑中心提取。采用了sobel算子和梯度倒数加权滤噪、最大类间方差法（Otsu）确定阈值、质心法提取光斑中心坐标。(The program is mainly realized under a dark background Gaussian noise spot containing noise filtering, to achieve spot center extraction. Using sobel operator and gradient inverse weighting filter noise, Otsu method (Otsu) to determine the threshold, the center coordinates of the spot centroid extraction.)

本段代码的功能是通过滤波器对细胞图像先进行预处理，然后再提取细胞中心实现细胞的追踪，准确率比较高。(This code is the function of cells through the filter of the first pre-process the image, then extract the cell center to achieve cell tracking, accuracy is relatively high.)

去散射和边缘增强是水下图像从严重细节损失、颜色偏移和模糊中提取的关键步骤。本文提出了一种增强水下图像对比度和边缘的新方法。(De-scattering and edge enhancing are critical procedures for underwater images which surfer from serious detail loss, color deviation and blurring. In this work, a novel method has been proposed to enhance contrast and edge of underwater images.)

指纹图像处理包括梯度处理、方向场处理、平滑处理、智能增强处理等(The fingerprint image processing including gradient processing, direction field processing, smooth processing and intelligent enhancement processing, etc)

2012年的压缩感知跟踪算法，修改了部分程序源码，可以运行。(2012 of compressed sensing tracking algorithm, modify some program source code, you can run.)

说明：  深度学习，读取二进制文件，识别手写数字。(Deep learning, reading binary files, recognizing handwritten numbers.)

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)