pipei

一种基于背景减法和帧差的运动目标检测算法(Based on background subtraction and frame difference moving target detection algorithm)

对比度拉伸，对原始图像进行对比度拉伸，从而得到拉伸后的图像(tensile contrast, the original image contrast stretching, and thus drawing the images)

说明：  直方图均衡化,直方图均衡化vc++源代码(histogramequalization)

基于图像处理，vc++平台的水果识别。通过亮度调整，梯度处理，边缘图像，边界跟踪，计算颜色形状等，计算出结果。(Based on image processing, fruit recognition vc++ platform. Through the brightness adjustment, gradient, edge image, boundary tracking, calculation of color shape, the calculated results. )

实现彩色图像的N*N 中值滤波器，可选择滤波系数。(To achieve the N* N color image median filter, selectable filter coefficients.)

图像处理的IPP库使用手册，包括每个函数的使用说明，以及参数的意义。(IPP library of image processing using the manual, including instructions for each function, as well as the significance of the parameters.)

该程序可以将一附图片中的人的脸部轮廓检测出来，以作后续处理。(The program can be attached to a film of people' s facial contours detected for follow-up.)

图形处理，Visual C++数字图像识别技术典型案例源代码 5(Graphics processing , the Visual C++ digital image recognition technology to a typical case of source code 5)

说明：  这是一个用C++语言实现一个指纹识别程序，要求满足FVC国际竞赛标准，并在学校建立的指纹库上实测。程序里应用到的技术有：中值滤波、直方图均衡化、脊线方向提取、Gabor滤波、指纹细化、特征提取、特征点过滤、基于局部特征点的特征匹配(This is a C++ Language realization of a fingerprint identification procedure, the requirements FVC meet international competition standards, and established in the school library on the measured fingerprint. Program applied to the technology are as follows: median filter, histogram equalization, the direction of the ridge line extraction, Gabor filtering, fingerprint thinning, feature extraction, feature point filtering, based on local feature points of the feature matching)

EDLines是一种快速直线检测算子，2012年在ICCV上提出，也是目前处理直线检测最快的算法之一，该算法包含三个步骤：（1）边缘提取：利用Edge Drawing (ED)算法[28,29]从灰度图像中提取边缘片段；（2）线段检测：利用最小二乘法提取直线段；（3）线段确认：遵循Helmholtz定律，从已提取的直线段中摒弃虚假线段。EDLines算法的优越性得益于Edge Drawing (ED)算法能够从灰度图像中准确、快速、稳定地提取出光滑、完整的边缘片段。Edge Drawing (ED)算法包含以下步骤： （1）采用size=5*5,σ=1的高斯核对灰度图像进行平滑滤波，去除噪声； （2）采用一种常用的梯度算子，如Prewitt、Sobel或Scharr等计算平滑后图像中每个像素点的梯度幅度和梯度方向； （3）将梯度图中邻域内幅度最大的像素点标记为锚点，这些锚点成为图像边缘点(edge elements)的概率很大； （4）将相邻的锚点连接成边缘线。从一个锚点起始，ED利用相邻像素的梯度幅度和方向在梯度为最大值的锚点之间游走。 (Edge Drawing (ED) is our recently-proposed, novel, fast edge detection algorithm. What makes ED stand out the existing edge detectors, e.g., Canny, is the following: While the other edge detectors give out a binary edge image as output, where the etected edge pixels are usually independent, discontinuous entities ED produces a set of edge segments, which are clean, contiguous, i.e., connected, chains of edge pixels. Thus, while the output of the other edge detectors requires urther processing to generate potential object boundaries, which may not even be possible or result in inaccuracies ED not only produces perfectly connected object boundaries by default, but it also achieves this in blazing speed compared to other edge detector.)