matlab图像处理

用matlab实现两幅彩色图像的无缝融合，输出结果为灰度图象(Using two color images matlab realize seamless integration, the output for gray-scale image)

calibration摄像机镜头的校正程序(calibration Calibration camera lens calibration procedure)

利用维纳滤波实现噪声中的语音信号恢复，产生chirp信号，噪声为高斯白噪声，噪声叠加在chirp信号上，构成观测信号x(n),使用编程方法实现使用维纳滤波滤除高斯噪声。(Wiener filtering the voice signal recovery in the noise generated chirp signal, the noise is Gaussian white noise, noise superimposed on the chirp signal, constitute the observed signal x (n), using the programming method using the Wiener filter filter Gaussian noise.)

这个算法是基于峭度的快速不动点算法，混合方式是线性瞬时混合，适定情况，该算法收敛速度较梯度算法快，很实用。(failed to translate)

在统计学中，皮尔逊积矩相关系数（英语：Pearson product-moment correlation coefficient，又称作 PPMCC或PCCs[1], 文章中常用r或Pearson s r表示）用于度量两个变量X和Y之间的相关（线性相关），其值介于-1与1之间。在自然科学领域中，该系数广泛用于度量两个变量之间的相关程度。它是由卡尔·皮尔逊从弗朗西斯·高尔顿在19世纪80年代提出的一个相似却又稍有不同的想法演变而来的。这个相关系数也称作“皮尔森相关系数r”。(In statistics, Pearson product-moment correlation coefficient (English: Pearson product-moment correlation coefficient, also known PPMCC or PCCs [1], the article used or Pearson s r r representation) is a measure of the two variables X and Y between correlation (linear correlation), a value between-1 and 1. In the natural sciences, the coefficient is widely used to measure the degree of correlation between the two variables. It is made a similar Karl Pearson Francis Galton in the 1880s but made a slightly different idea evolved. The correlation coefficient is also called Pearson s correlation coefficient r .)

基于CCS开发平台，关于IT公司的TMS320DM642的开发实例（包括，串口、UART接口、图像灰度显示、图像阈值分割，高斯平滑）等(the program example based on CSS of IT MS320DM642 include GPIO UART Gaussscale Grayscale Threshold)

球面投影,用于图像空间数据到目标坐标系下的映射(sphere project)

kmeans算法实现，对图像处理。亲自使用过，可用，供参考。(Kmeans algorithm for image processing.)

纹理图像分割的算法和实例，其中包括理论的讲解和 MATLAB 文件。(Algorithms and Examples of Texture Image Segmentation)

说明：  GUI界面实现打开图像，基于阈值分割，基于边缘分割，基于形态学分割，基于种子点分割和基于主动轮廓模型分割(The GUI interface can open the image, based on threshold segmentation, edge segmentation, morphological segmentation, seed point segmentation and active contour model segmentation)