QGA

灰度图生成程序 可以生成BMP/JPG格式的灰度图 大家可以随便使用下载(Grayscale generator can generate BMP/JPG format, you can easily use the download grayscale)

对灰度图像或RGB图像，进行基于边缘检测和区域生长的图像分割技术(On grayscale images or RGB images, for edge detection and region growing-based Segmentation)

transver mat file format to raw file format, simply matlab code, usd to 3D medical image processing/data input

提出了一种基于图像边缘连接的线段检测方法。数字图像中的线段是由很多线段图元组成,在同一 条线段中,它们的方向是相同的并且受到真实线段的方向约束。因此可以通过提取小的线段图元, 然后分析它们 的连接特性, 进行线段的检测。首先用ADM 算法对图像进行边缘检测, 得到单像素宽的边缘 然后在4 个方向上 进行线段的检测 对初步得到的结果进行合并和验证, 去掉短的线段, 连接临近的线段。经过实验证明, 该方法能 够快速地提取图像中的线段,并且对噪声具有一定的鲁棒性(A line segment connecting a detection method based on image edges. The segment of the digital image is composed by many segments elements, their orientation is the same and by the real line segment direction constraint in the same segment. By extracting a small segment Element can then analyze their connection characteristics for the detection of the line segment. First with ADM algorithm for edge detection on the image to obtain a single-pixel-wide edge then in the four directions on the detection of the line segments merger and verification of the preliminary results obtained, removing the short line segments, the line segments connected near. The experiments show that this method can quickly extract the line segments in the image, and certain robustness to noise)

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)

检测道路边缘，利用道路的平行线特性，来检测道路所在直线。(Detect edges of the road, the road parallel to the line features to detect the road where the straight line.)

换热器模型的MATLAB实现,可用于工程计算,还可以用于教学演示(Heat exchanger model of MATLAB and can be used for engineering calculation, and can also be used for teaching demonstration)

随机森林用于 图像处理matalab 代码(Random Forest matalab code for image processing)

题目： 八宝粥的生产过程中需要用到大量的红豆（红蚕豆），而实际上从红豆产地购买得到的红豆中会参杂一些杂物和不合格红豆。为了保证八宝粥的质量，需要对红豆进行筛选，去除不合格红豆和其它杂物。目前国内在红豆筛选工序上仍然使用人工筛选的方式，存在人工成本高、精确度差、劳动强度大等缺点。因此八宝粥生产厂商迫切需求开发一款能够自动实现红豆筛选的自动化筛选机。针对上述需求，请设计筛选机中的基于可见光图像的红豆筛选算法，该算法应尽可能的准确的找到参杂在红豆原料中的不合格红豆和杂物。 附件包括两个不同实验环境下拍摄得到的红豆相关图片样本库，实验环境分别为自然光照条件和灯光条件。每个条件下的样本库包括合格、不合格、杂物和混合四类样本的图像，其中合格、不合格以及杂物的图像可用来训练或建模，混合类图像用来测试算法的准确性和实时性。同学可在自然光照环境下的样本库或者灯光条件下的样本库中任选其一，用于解决上述问题。 作业提交格式包括以下四个内容： 一、 问题分析 二、 算法设计 三、 实验结果(将自己选择的样本库中的混合样本图片中的样本个体进行分类，并给出可视化的分类结果。) (Title:Use a lot of red bean production process of eight-treasure porridge ( red bean ), but in fact from the Red areas available in red bean can blend some sundries and unqualified red bean. In order to ensure the quality of eight-treasure porridge, the need for screening of red bean, red bean and other debris removal is not qualified. At present in the domestic bean screening process still use manual screening method, has the disadvantages of high labor costs, poor accuracy, high labor intensity. Therefore, eight-treasure porridge manufacturers need to develop a can achieve automatic bean screening automatic screening machine. According to the above requirements, please design screening machine selection algorithm based on visible light image of the red, the algorithm should be as accurate as possible to find the mixed in the bean raw materials in the unqualified red bean and debris.Accessories include captured in two different experimental environment of the red bean image sample dat)

选择合适的样本特征点，然后可以将特征导入svm进行分类(After the image processing, the main information is obtained by PCA transform, and then the feature of texture information selection is put forward)