curvematchingtool

高光谱遥感影像数据降维处理 包括线性降维和非线性降维处理(Hyperspectral remote sensing image data to reduce the dimension of linear dimensionality reduction and nonlinear dimensionality reduction processing)

实现图像渐晕效果的模拟，输入一副分辨率大于800×500的bmp图像，可以得到模拟出渐晕效果后的图像，参数可调(The image vignetting effect simulation, input a bmp image resolution is greater than 800,500, and adjustable parameters can be simulated image vignetting effect)

说明：  这是“An efficientlocalChan–Vesemodelforimagesegmentation”（简称LCV模型）的MATLAB源代码。LCV模型是非常重要局部区域活动轮廓模型，它被广泛使用于各个领域，如MRI大脑图像分割，血管图像分割，图像偏差场纠正。(This is "An efficientlocalChan-Vesemodelforimagesegmentation" (referred to as the LCV model) of the MATLAB source code. LCV model is very important to local area active contour model, which is widely used in various fields, such as MRI brain image segmentation, vessel segmentation, image bias field correction.)

Markov随机场的例子程序，包括ICM，BP算法，matlab编写，共30几个函数。(Markov random field examples of procedures, including ICM, BP algorithm, matlab prepared a total of 30 number of functions.)

Z源逆变器开路三相SVPWM调制, 注入了三次谐波(Open-circuit Three-phase SVPWM Modulation of Z-source Inverter)

细胞核分割计数，通过大津法阈值分割图像后，进行腐蚀以及膨胀等操作处理图像，标记连通区域来计数(Threshold segmentation count of nuclear image)

gabor小波变换提取图像纹理信息，得到一维向量。(gabor wavelet transform to extract image texture information)

基于mfc对话框开发的图片位深度转换器，源码部分截图如下。 CImage img;    img.Load("D:\qrcode.png");    img.Save("D:\qrcode.bmp");    CxImage ximage;    ximage.Load("D:\qrcode.bmp", CXIMAGE_FORMAT_BMP);    ximage.AlphaStrip();    ximage.Save("D:\11.bmp", CXIMAGE_FORMAT_BMP);

MTF计算程序，图像源需为倾斜边缘，能快速计算不同倾斜边缘的MTF曲线。(MTF calculation procedure，Sequence image source need for inclined edge，Can quickly calculating different inclined edge of MTF curve. )

Image segmentation systems have the potential to dramatically improve the performance of Digital image processing. Segmentation procedures partition an image into its constituent parts or objects. In general, segmentation is one of the most difficult tasks in digital image processing. Detection of salient image regions is useful for applications like image segmentation, adaptive compression, and region-based image retrieval. This problem can be tackled by mapping the pixels into various feature spaces, which are subjected to various grouping algorithms It present a novel method to determine salient regions in images using low-level features of luminance and color. The method is fast, easy to implement and generates high quality saliency maps of the same size and resolution as the input image.