DelTilt

SIFT特征匹配算法是目前国内外特征点匹配研究领域的热点与难点，其匹配能力较强，可以处理两幅图像之间发生平移、旋转、仿射变换情况下的匹配问题，甚至在某种程度上对任意角度拍摄的图像也具备较为稳定的特征匹配能力。该算法目前外文资料较多，但中文方面的介绍较少.(SIFT feature matching algorithm is at home and abroad feature points matching hot area of ​ ​ research and difficulties in its matching capability that can handle between the two images, translation, rotation, the matching problem in the case of affine transformation, and even to some extent have a relatively stable feature of the ability to match images taken on any angle. The algorithm is more foreign materials, but the Chinese introduction less.)

PCNN源代码,此为简化PCNN模型的实现原代码。(the source code of pcnn)

读取单幅envi图像格式的img图像，并将其显示出来(read img)

为图像添加各种噪声，如高斯噪声、泊松噪声、椒盐噪声等。（需有openCV）(Add all kinds of noise for the image, such as Gaussian noise, Poisson noise, salt and pepper noise. (The need for openCV))

以心电信号检测为例，通过小波模极大值奇异性检测和求取Lipschitz指数确定心电信号R波的位置(Taking the ECG signal detection as an example, the position of the R wave of the ECG signal was determined by the singularity detection of the wavelet modulus and the Lipschitz index)

对细胞进行分割和追踪的代码，细胞是植物细胞，其中的两篇论文是局部图分割的经典论文，值得一看(To segmentation and tracking code of cells, cells is plant cell, two of the paper is a classic papers of local map segmentation, worth a look)

说明：  基础图像处理语言，RGB各图层提取，量化图像，lena典型案例代码。(Basic image processing language, RGB layer extraction, quantization image, Lena typical case code.)

给出了偏微分方程的径向基函数配点法的数值解法，进行了数值模拟，并且给出了图形(The numerical solution of the radial basis function with partial differential equation method, numerical simulation, and gives the graphic )

说明：  二值化处理图像，自动拟合椭圆并得到长短轴的大小。(Automatically fit the ellipse and get the size of the long and short axes)

直方图均衡化，包括动态直方图均衡化，全局直方图均衡化，局部直方图均衡化，以及绘制图像的直方图 其中动态直方图均衡化用A dynamic histogram equalization for image contrast enhancement 只取x=0情况(Histogram equalization, including dynamic histogram equalization, global histogram equalization, local histogram equalization, and where to draw the image histogram dynamic histogram equalization with A dynamic histogram equalization for image contrast enhancement only take x = 0 Happening)