资源列表
figure
- 本程序可以用来进行图像的锐化，滤波，大小设置和旋转，还有进行红绿蓝成分改变以及改变方位角和仰角的程序。-This procedure can be used for image sharpening, filtering, sizing and rotation, as well as red, green and blue components were changed and the change in azimuth and elevation program.
chepaishibie
- 车牌识别全部，包括matlab代码和自制的模板。求接纳！-License plate recognition all, including the matlab code and homemade templates
yundongmubiaojiance
- 关于运动目标检测基础算法，混光流法合高斯模型、均值法、及帧间差分法-Moving target detection algorithm based on mixed Gaussian model optical flow together, mean method, and the inter-frame difference method
shuzishuibiao
- 数字图像识别研究，为图像研究报告,shui biao lunwen -tuxiang shu zi shi shuibiao shi bie
invansc_v3
- 图像处理，图像降噪优化处理，视频处理，视频降噪方法处理-Image processing, image optimization noise reduction processing, video processing, video noise reduction processing method
iterVSTpoisson_STANDALONE
- 图像处理，算法处理，图像美化，图像变换，去噪处理，锐化处理-Image processing algorithm processing, image beautification, image conversion, de-noising, sharpening
VapourSynth-BM3D-master
- VapourSynth-BM3D-master BM3D算法的原创代码，运行速度超快，参考文献《Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering》-VapourSynth-BM3D-master BM3D original algorithm code, run super-fast, Reference " Image Denoising by Sparse 3-D Transform-Doma
Improved-hybrid-Gauss
- 本文主要是针对传统混合高斯模型运动目标检测算法的不足做出了一些改进-This article is for the shortcomings of traditional Gaussian mixture model moving object detection algorithm made some improvements
Fisher1
- Fisher线性判别函数是研究这类判别函数中最有影响的方法之一。对线性判别函数的研究就是从R.A.Fisher在1936年发表的论文开始的。-Fisher linear discriminant function discriminant function is one of the most influential of these methods research. Research on linear discriminant function is RAFisher published
Opencv_Mouse_ZuoBiao
- 应用vs2013MFC,opencv实现识别鼠标坐标，可直接使用-Applications vs2013MFC, opencv achieve recognition mouse coordinates, can be used directly
sparseMRI_v0.2
- Sparse MRI The application of compressed sensing for rapid MR imaging-Sparse MRI The application of compressed sensing for rapid MR imaging
Bayesian-CS-Laplace-Priors
- Bayesian Compressive Sensing Using Laplace Priors