Imagesregistered

基于堆栈的图像分割算法，速度较普通区域生长法更快(Stack-based image segmentation algorithm, region growing faster than the ordinary method faster)

基于matlab的语言的汽车牌照显示，其中内容主要是和数学形态学相关的操作。(Matlab language based on license plate display, which were mainly related to the operation and mathematical morphology.)

实现地震资料的多次波处理,实现相同点的叠加，进而切除(to complish the goal that the noise can be suppressed in the seismic data)

说明：  该程序采用matlabGUI编写,包含了经典的图像匹配算法.(MatlabGUI prepared using the program, including the classic image matching algorithm.)

说明：  用于粒子图像测速技术的图像处理，分析得到流场信息(Image Processing for Particle Image Velocimetry and Flow Field Information Analysis)

图像分割源码，是一种基于图论的方法做的。分割的质量超好，不信的话下载下来看，你就清楚了！(Image segmentation source code, is a method based on graph theory to do. Segmentation quality is super good, do not believe it download the next run, you will be clear!)

基于CFAR虚警率的图像分割算法，可快速对灰度图像进行CFAR检测分割。(Based on the false alarm rate of CFAR image segmentation algorithm, the gray image can be fast CFAR detection and segmentation.)

IMM算法，用于机动目标的精确跟踪和定位，该模型集考虑了三种典型子模型:CV,CT,CA. (interacting multiple model (IMM) code for target tracking )

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.)

compressing the image using JPEG 2000 compression coding to get 10:1 ratio