资源列表
rlfft
- 用N点复序列快速傅里叶变换来计算2N点实序列的离散傅里叶变换。-N-point complex sequence with a fast Fourier transform to calculate the real sequence 2N point discrete Fourier transform.
LungSeg
- 肺部CT图片分割提取出肺实质：迭代法获取灰度阈值；二值化图像；分区使用纯M语言编写，各个环节都是自己编程解决，没有用Matlab内置的函数，方便改成其他语言代码。-Lung CT image segmentation to extract the lung parenchyma: iterative methods for grayscale threshold binary image partition using pure M language, are all aspects
high-resolution-sar
- 高分辨率及新模式SAR的成像算法，含有圆轨迹快速成像和压缩感知等内容-High resolution and the new mode of SAR imaging algorithm, containing circular trajectory fast imaging and compressed sensing and other content
Smoke-detector
- 关于图像中的烟雾检测，输入图像，如果图像有烟雾的话，会自动检测，并显示出来-The smoke detector on the image, the input image, if the image of smoke, it will automatically detect and display
Sparsity-Collaborative-Track
- 基于稀疏表示的目标跟踪,对于稀疏表示应用于图像处理的同志可是一个借鉴。-Robust Object Tracking via Sparsity-based Collaborative Model
3_demosaicing
- 图像demasaicing 方法， 两种一种是线性，一种是nearest-two ways to demasaicing, nearest and linear, the image is bayer
PictureManger
- 图片管理系统，实现图片的查找、简单的以图搜图、编辑、批量操作、灰度、均衡、放大缩小等简单功能。-Image management system
Image_segmentation
- 本程序实现了Matlab 基于SOFM（自组织特征映射神经元网络）颜色聚类图像分割。-This application implements the Matlab based on SOFM (self-organizing feature map neural network) color clustering image segmentation.
adaptivethreshold
- 与坐标相关的阈值也叫动态阈值、局部阈值、自适应阈值，局部阈值法：自适应图像分割。-Threshold associated with coordinates, also called dynamic threshold, local threshold, adaptive threshold, local threshold method: adaptive image segmentation.
MATLAB
- 有关数字图像退化函数的估计与滤波，维纳滤波器实现，运动模糊等-For digital image degradation function estimation and filtering, Wiener filter implementation, motion blur, etc.
monituihuo
- 本程序用模拟退火算法实现了图像的单阈值分割，分割效果良好。-The procedures used simulated annealing algorithm to achieve a single image threshold segmentation, segmentation with good results.
SVM
- 一个简单易学的SVM程序，包括线性和非线性SVM，并包含二维和三维图像的转化。-An easy to learn SVM procedures, including linear and nonlinear SVM, and includes the conversion of two-dimensional and three-dimensional images.