qianfangjh

ImageView: This is an image viewer program

说明：  图像边缘检测及图像区域分割、目标检测、目标识别(Image edge detection, image region segmentation, target detection and target recognition)

说明：  经典的BM3D算法，针对图像去噪处理，完整的Matlab代码(Classic BM3D algorithm)

imx377 手册 imx377 datasheet imx377 datasheet(imx377datashee, imx377datasheet,imx377datasheet)

说明：  基于人体手指静脉图像分割算法的研究(Research on segmentation algorithm of human finger vein image)(Research on segmentation algorithm of human finger vein image)

使用Matlab生成一维光栅条纹，可用于结构光三维重建。(Matlab Generate One Dimensional Grating)

说明：  使用中值，均值，小波，DCT，PCA五种方法实现对图像的去噪处理。(Five methods, median, mean, wavelet, DCT and PCA, are used to denoise the image.)

利用广义S变换求取地震信号时频谱，参数可调，代码清晰，用途广泛(The generalized S transform is used to get the time spectrum of signals, and the parameters can be adjusted.)

说明：  用小波的方法对图像进行二值化操作÷ 效果不错(wavelet methods of image binarization good operating results )

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.)