duibiduzengq

visionpro字符识别

利用迭代残差插值的去马赛克算法，进一步减小估计误差，得到更准确的全彩色图像，性能很不错。(Iterative interpolation residuals demosaicing algorithm to further reduce the estimation error, more accurate full-color images, the performance is very good.)

matlab图像处理代码，利用分裂、合并算法进行图像的分割，含m文件和GUI(matlab image processing code, using split, merge image segmentation algorithms, including documentation and GUI m)

简单写的一些图像预处理（灰度增强、小波高通滤波、LBP、迭代阈值分割、种子生长法etc）和RGB 、纹理（相关矩阵）特征提取(Write some simple image pre-processing (grayscale enhancement, wavelet high-pass filter, LBP, iterative threshold segmentation, seed growth method etc) and RGB, texture (correlation matrix) feature extraction)

一种在二维、三维数据中利用Canny算子检测边缘的边缘检测器(A Canny edge detector for two-dimensional image and three-dimensional volume data.)

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.)

提取特征点并匹配，有基于相关系数和最小二乘两种方法。(Extracting feature points and matching)

本文首先对数字全息技术的记录和再现的基本原理、数值重建算法、频谱滤波方法，数字全息图的记录条件、再现光场的分离条件等基本问题进行了分析讨论(Firstly, the basic principle of the recording and reproducing of the digital holography, numerical reconstruction algorithm, the spectral filtering method, the digital hologram recording conditions, reproducing conditions of separation of the light field, and other basic issues discussed)

2009年CVPR最佳论文（暗通道去雾算法）及其MATLAB代码(The best paper of 2009 CVPR conf.and its corresponding Matlab code. very useful for us.)

运用Perona-Malik各向异性扩散模型对3d图像进行平滑去噪。此方法可以有效的抑制高斯噪声，并保护图像的边缘特征。(The use of Perona-Malik anisotropic diffusion model for smooth 3d image denoising. This method can effectively suppress Gaussian noise and to protect the image of the edge feature.)