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Description: 实验中发现opencv的cvCaptureFromCAM 使用的是vfw,采用消息机制,速度较慢,测试发现fps只有 9-12左右,太慢了. 发现经过使用directshow后速度提升到60帧/s.在opencv group上了解到这是一个普遍问题,也许有人做过转换,却没有完整的例子与代码.在此贴出.对希望提高opencv视频分析速度的有所帮助-Opencv experiment found that the cvCaptureFromCAM using vfw, the use of information mechanism to slow fps test found that only about 9-12, too slow. Discovered through the use of DirectShow velocity to 60/s. In opencv group understanding that this is a common problem, perhaps someone has done the conversion, but no complete examples and code.此贴out in. opencv want to improve the speed of video analysis help
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Size: 2048 |
Author: er |
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Description: 基于opencv图像处理库的傅里叶变换，首先说明是pdf文件，但源码运行过了，是正确的。-Opencv image-processing library based on the Fourier transform, first of all that are pdf files, but source code to run before, are correct.
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Size: 44032 |
Author: 巢歌 |
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Description: 二维线性鉴别分析(2DLDA)算法能有效解决线性鉴别分析(LDA)算法的“小样本”效应，支持向量机
(SVM)具有结构风险最小化的特点，将两者结合起来用于人脸识别。首先，利用小波变换获取人脸图像的低频分量，忽
略高频分量：然后，用2DLDA算法提取人脸图像低频分量的线性鉴别特征，用“一对多”的SVM 多类分类算法完成人脸
识别。基于ORL人脸数据库和Yale人脸数据库的实验结果验证了2DLDA+SVM算法应用于人脸识别的有效性。-”Small sample size”problem of LDA algorithm can be overcome by two—dimensional LDA f 2DLDA)，and
Support Vector Machine(SVM)has the characteristic of structural risk minimization．In this paper，two methods were
combined and used for face recognition．Firstly，the original images were decomposed into high—frequency and low—frequency
components by Wavelet Transform(WT)．The high—frequency components were ignored，while the low—frequency components
can be obtained．Then．the liner discriminant features were extracted by 2DLDA，and”one VS rest”。strategy of SVMs for
muhiclass classification was chosen to perform face recognition． Experimental results based on ORL f Olivetti Research
Laboratory1 face database and Yale face database show the validity of 2DLDA+SVM algorithm for face recogn ition．
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Size: 236544 |
Author: 费富里 |
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Description: 用于双目立体图像校正：亚像素角点、双向粗略匹配、用RANSAC方法计算F、opencv自带函数矫正图像-For binocular stereo image correction: the corner points of the sub-pixel, two-way rough match, use the RANSAC method F comes with opencv function corrected image
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Size: 3072 |
Author: guonan |
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Description: VC环境下图像的QR分解
需要先配置OPENCV-VC environment of image QR decomposition
You need to configure OPENCV
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Size: 1024 |
Author: liuliu |
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Description: 调用opencv的cvFindFundamentalMat函数求得基本矩阵F，利用绘图板指针pDC将极线绘制出来。-The cvFindFundamentalMat call opencv functions to obtain the fundamental matrix F, use a graphics tablet pointer pDC the lines drawn.
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Size: 2469888 |
Author: 卜帆 |
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Description: opencv实现傅里叶变换,傅立叶变换是把图像从空间域转化到频率域的变换。空间域：一般的情况下，空间域的图像是f（x，y）=灰度级（0-255），形象一点就是一个二维矩阵，每个坐标对应一个颜色值。频率域：频率：对于图像来说可以指图像颜色值的梯度，即灰度级的变化速度，幅度：可以简单的理解为是频率的权，即该频率所占的比例：能量=幅度（可能不太准确），变换结果为F（u，v），F代表幅度值，u代表x方向的频率，v代表y方向的频率(Fu Liye transform is the transformation of image from space domain to frequency domain. Spatial domain: in general, the image in the spatial domain is f (x, y) = gray level (0-255), and the image is a two-dimensional matrix, and each coordinate corresponds to a color value. Frequency domain: frequency: for images can refer to gradient image color values, i.e. the rate of change of gray level range can be simply understood as the right that the frequency of the frequency, the proportion of energy (= range may not be accurate), transform results for F (U, V), F on behalf of amplitude values, u represents the direction of X V frequency, Y direction frequency)
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Size: 12288 |
Author: ambition123
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