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test psnr
- 视频处理中最重要的指标：PSNR(峰值信噪比)。本人写的一个小程序，用于测试图象中的Y、U、V分量的PSNR值。-PSNR is a very important index in video disposal. there are a little program . It tests the PSNR of the YUV in the image.
1D gabor Filter
- Descr iption: Recent studies on Mathematical modeling of visual cortical cells [Kulikowski/Marcelja/Bishop:1982] suggest a tuned band pass filter bank structure. These filters are found to have Gaussian transfer functions in the frequency domain. Thu
harly
- 建议在linux下运行,这个是个小丑模型,通过读取MD3来绘制,并且做出镜子的效果.运行方式:make (编译) ./md3 (运行) 按键控制: u(上肢动作变换), l(下肢变换动作)-proposed running under Linux, this is a clown model, MD3 to read through mapping, and make a mirror effect. Operation : make (compiler) ./md3 (run) but
SIGGRAPH97-Course-Notes-Processing-Messages&Using-
- Nate Robins教程之Processing of user input Win32 Messages and Menus allow for processing of user input. Methods for intercepting and responding to messages as well as methods for using menus is presented below.-Nate Robins Guide Zhi Processing of user
ImageWatershedSegmentation
- THE SOFTWARE IS PROVIDED \"AS-IS\" AND WITHOUT WARRANTY OF ANY KIND, EXPRESS, IMPLIED OR OTHERWISE, INCLUDING WITHOUT LIMITATION, ANY WARRANTY OF MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE IN NO EVENT SHALL RUTGERS UNIVERSITY BE LIABLE FOR
ff11datparser
- ff11datparser ff11 tools full set source used for decode ff11 client graphic source-ff11datparser ff11 source tools full set u sed for decode ff11 client graphic source, and,
CardRecognization
- 车牌识别系统 使用说明 使用时打开此例题目录下pic中的图片，然后依次单击按钮“转”、“1”、“2”、“3”、“4”和“5”，就可以实现精确的车牌定位。 具体步骤 1.24位真彩色->256色灰度图。 2.预处理:中值滤波。 3.二值化:用一个初始阈值T对图像A进行二值化得到二值化图像B。 初始阈值T的确定方法是:选择阈值T=Gmax-(Gmax-Gmin)/3,Gmax和Gmin分别是最高、最低灰度值。
365Rose
- SDK写的365朵玫瑰随机出现的算法,送给朋友是很漂亮的美境,希望大家多多改进-SDK to write the 365 roses were the algorithm, for a friend is a very nice U.S. territory, I hope Members can improve
ImprolaLib-0.0.2
- Improla == IMage PROcessing LAb. It is meant for image processing research and teaching. From ImprolaLib-0.0.2, it supports reading and writing PNG images. It uses PNGwriter(pngwriter.sourceforge.net) for this purpose.-Improla == IMage PROcessing LAb
reply_1_1007847
- 车牌定位使用说明 使用时打开此例题目录下pic中的图片，然后依次单击按钮“转”、“1”、“2”、“3”、“4”和“5”，就可以实现精确的车牌定位。 具体步骤 1.24位真彩色->256色灰度图。 2.预处理:中值滤波。 3.二值化:用一个初始阈值T对图像A进行二值化得到二值化图像B。 初始阈值T的确定方法是:选择阈值T=Gmax-(Gmax-Gmin)/3,Gmax和Gmin分别是最高、最低灰度值。 该阈值对不同牌照有一
yuv_filter
- yuv视频中y、u、v三个分量通道分别进行各种滤波-yuv Video y, u, v 3 components were various channel filtering
read_yuv
- 自己写的用matlab读取YUV文件,并分帧保存.生成一个N维矩阵(N是总帧数),一维就是一帧的数据. Y U V分别保存.-wrote it myself using Matlab YUV read documents, preservation and framing. Generating an N-dimensional matrix (N is the total frame count). is a one-dimensional data. Y U V were preserv
capturecamera
- How to use OpenCV to capture and display images from a camera ，i do believe it do good to u!-How to use OpenCV to capture and display ima celebrated from a camera, i do believe it do good to u!
vc.rar
- U.ARE.U指纹采集仪基于VC的指纹识别源码！,Miriam U.ARE.U fingerprint of fingerprint identification based on VC-source!
yv12_y 分量显示器
- YUV分量显示程序。可分别显示Y，U，V 分量。对于理解YUV机制有很好的帮助。-YUV component display program. May showed the Y, U, V components. YUV mechanism for understanding a very good help.
Grad-U-and-moving-rules
- 计算梯度向量Grad U和圆饼的移动规则:将所有的力都正因分解，分解成x轴方向和y轴方向的力，整个问题就简单化了，通过分别求出x轴方向和y轴方向的力之和，就可以知道该圆饼将要移动的轨迹，而x轴方向和y轴方向的合力方向应该就是该圆饼的梯度向量的方向。而所有圆饼的梯度向量就组成了Grad U-Because of all the forces are decomposed into the x-axis direction and the y-axis direction of the force,
U.are.U_4000-SDK
- 指纹仪SDK 指纹仪SDKU.are.U_4000 SDK.txt指纹仪SDK指纹仪SDK指纹仪-U.are.U_4000 SDK.txtU.are.U_4000 SDK.txt
U
- U系统算法。 简单的算出N表达式，并画出图像-U system algorithms. Simple expressions for calculating N, and draw the image
unet-pytorch-master
- 使用Pytorch搭建U-Net，该模型可以对随机传入任意大小的图片进行图片分割，根据所训练的数据和标签得到索要分割的区域。(Using Python to build u-net, the model can segment random incoming pictures of any size, and get the region to be segmented according to the trained data and labels.)
Unet
- UNet最早发表在2015的MICCAI上，短短3年，引用量目前已经达到了4070，足以见得其影响力。而后成为大多做医疗影像语义分割任务的baseline，也启发了大量研究者去思考U型语义分割网络。而如今在自然影像理解方面，也有越来越多的语义分割和目标检测SOTA模型开始关注和使用U型结构，比如语义分割Discriminative Feature Network(DFN)(CVPR2018)，目标检测Feature Pyramid Networks for Object Detection(FP