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harris
基于harris兴趣点的图像检索程序。先特征提取，进而进行特征匹配，最后还有结果的返回。(Harris points of interest based on image retrieval process. First feature extraction, feature matching and then carried out and, finally, the results returned.)
- 2009-11-08 13:53:40下载
- 积分：1
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image_processing
图像处理入门编程
包括动态跟踪和视频处理
有对应的任务指导书(image processing
include the mission book)
- 2018-03-29 00:52:33下载
- 积分：1
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信息熵
说明： 基于信息熵进行图像质量评价，评价图像质量好坏。流程简单易于操作(Image quality evaluation based on information entropy)
- 2020-10-19 21:55:52下载
- 积分：1
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rawelroperty
Matlab入门级图像处理程序 低通滤波器对受噪声干扰的图像进行平滑处理,(Matlab entry-level image processing low-pass filter to smooth the image by noise,)
- 2017-05-16 12:46:17下载
- 积分：1
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彩色图像的合成vc代码
说明： 一个较好效果的彩色图像合成代码(分左右两个视窗,点击左边的视窗后显示左图,选择操作后,点击右视窗,可看到各种不同合成操作的效果图)(a good result Color Image Synthesis code (around two windows, Click on the left side of the window shows the municipality, the choice of operation, click on the right window we can see various synthesis operating results))
- 2006-02-22 23:06:48下载
- 积分：1
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jiandanronghe
小波变换方法采用对高频系数平均的方法实现图像融合算法(Wavelet transform coefficients using the average of the high-frequency method to achieve image fusion algorithm)
- 2009-10-24 11:48:15下载
- 积分：1
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GanssianBlur
说明： 平台基于visual studio2015，使用opencv函数实现高斯滤波，进行图像处理(gaussian blur based on opencv)
- 2020-06-20 05:40:01下载
- 积分：1
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instfreq
亲自测试 完全可以运行且有效，求瞬时频率，各个版本的matlab 都ok(Test yourself Can be run completely and effectively)
- 2017-11-05 16:10:35下载
- 积分：1
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Desktop2
Kalman滤波在船舶GPS定位导航系统中的应用(Application of Kalman Filter in Ship GPS Positioning and Navigation System)
- 2018-07-19 09:31:28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
