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车牌字符识别
使用MATLAB语言实现简单的字符识别，使用神经网络算法，有效的识别车牌(Simple Character Recognition Using MATLAB Language)
- 2018-12-06 20:23:25下载
- 积分：1
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Level.Set.Methods.And.Dynamic.Implicit.Surfaces
Level Set Methods and Dynamic Implicit Surfaces - Ronald Fedkiw(Level Set Methods and Dynamic Implicit Surfaces- Ronald Fedkiw)
- 2021-02-16 12:59:47下载
- 积分：1
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mbtropclitan
一个较好效果的彩色图像合成代码(分左右两个视窗,很好用(A good effect of color image synthesis code (about two Windows, very useful)
- 2017-05-07 10:53:15下载
- 积分：1
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RobustPCA-master
低秩学习，rpca，适用于图像去噪，视频跟踪。(low rank learning,rpca,image denoise.Image tracking)
- 2021-01-05 20:18:55下载
- 积分：1
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Texture_Synthesis_Image_Inpainting-master
通过基于纹理的图像修复算法对具有较大的破损区域的图像进行了修复；MATLAB代码(Texture based image restoration,MATLAB)
- 2021-04-19 09:58:51下载
- 积分：1
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batch-replacement-Note-CAD
程序可实现dwg文件注记的批量替换, 可根据需要进行扩展。(Dwg file program allows batch replacement Note)
- 2010-06-06 23:33:18下载
- 积分：1
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growcut
这是关于图像区域生长的源码，希望对这方面的人有所帮助(This is on the source image region growing, I hope people in this area help)
- 2008-06-06 09:06:27下载
- 积分：1
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FuzzyClustering
模糊聚类程序，并且有文档，详细的给出了模糊聚类算法的各个步骤。(Fuzzy clustering procedure, and there are documents, is given in detail the various steps in fuzzy clustering algorithm.)
- 2009-11-16 06:49:56下载
- 积分：1
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FBG
说明： 对光纤布拉格光栅进行matlab出图，光纤布拉格光栅也称光纤光栅，简称FBG(use matlab to FBG figuer)
- 2019-12-12 08:29:12下载
- 积分：1
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PCA
主成分分析 （ Principal Component Analysis ， PCA ）或者主元分析。是一种掌握事物主要矛盾的统计分析方法，它可以从多元事物中解析出主要影响因素，揭示事物的本质，简化复杂的问题。计算主成分的目的是将高维数据投影到较低维空间。给定 n 个变量的 m 个观察值，形成一个 n ′ m 的数据矩阵， n 通常比较大。对于一个由多个变量描述的复杂事物，人们难以认识，那么是否可以抓住事物主要方面进行重点分析呢？如果事物的主要方面刚好体现在几个主要变量上，我们只需要将这几个变量分离出来，进行详细分析。但是，在一般情况下，并不能直接找出这样的关键变量。这时我们可以用原有变量的线性组合来表示事物的主要方面， PCA 就是这样一种分析方法。(Principal component analysis (Principal Component Analysis, PCA) or PCA. Is a statistical method to grasp the principal contradiction of things, it can be resolved diverse things out the main factors, revealing the essence of things, simplifying complex problems. The purpose of calculating the main component of high-dimensional data is projected to a lower dimensional space. Given n variables of m observations, forming an n ' m of the data matrix, n is usually large. For a complex matters described by several variables, it is difficult to know, so if you can grab something to focus on key aspects of analysis? If the main aspects of things just reflected on several key variables, we only need to separate out these few variables, for detailed analysis. However, in general, does not directly identify this critical variables. Then we can represent the major aspects of things with a linear combination of the original variables, PCA is one such analysis.)
- 2021-01-28 21:48:40下载
- 积分：1
