ONSL0

图像拼接技术，图像配准技术，初步实现有重合区域的两幅图像的配准(Image mosaic technology, image registration, the initial overlap region to achieve a registration of two images)

本程序是基于Matlab的米粒图像的分割、计数以及周长、面积、形状因子等参数的计算。图像分割是基于腐蚀膨胀算法，选用边界跟踪法计算周长，程序代码清晰明了，对于参数的计算速度较快，且参数计算精度较高。(This procedure is based on the Matlab image segmentation grain of rice, counting and perimeter, area, shape factor and other parameters of the calculation. Image segmentation is based on the corrosion expansion algorithm, the choice of boundary tracking method perimeter, code clarity, the parameter calculation is faster and higher precision parameters.)

在已知高度的情况下利用单目视觉摄像头来进行定位。(Positioning with a monocular vision camera at a given height)

说明：  信号处理傅里叶变换，三分之一倍频程，荷载时程曲线绘制(Signal processing Fourier transform, one third octave, load time history curve drawing)

分为行人检测，特征提取，行人跟踪三步，其中最主要的代码也是这个代码的中心是行人跟踪这一部分，我采用的是kalman滤波器进行行人下一帧位置的预测，可以很好的提高行人跟踪的鲁棒性，跟踪结果比较准确。(Into pedestrian detection, feature extraction, pedestrian tracking steps, which is the center of the main code of the code is pedestrian tracking this section, I kalman filter is used to predict the position of the next frame pedestrians, can be a good improvement pedestrian tracking robustness, tracking results more accurate.)

这是一个数字图像处理常用的功能的集成软件，用VC++ 编写，可以对图像进行一些常见的操作，也可以自己扩展功能(This is a common feature of digital image processing integrated software, images can perform basic operations)

说明：  以图搜图，深度学习，采用vgg16,对应文件夹放入图片即可使用(image_retrieval_platform-master)

off文件读取绘图函数，能读取顶点坐标和拓扑关系，并以此绘制三维模型图像，使用可以直接调用，需要注意参数的类型，只能读要取off(Off file to read the drawing function, can read the vertex coordinates and topological relations, and in order to draw the three-dimensional model images, the use of direct calls, you need to pay attention to the type of parameters, can only read to take off )

利用matlab从某张图片中提取或删除某种颜色的背景。(extract/delete certain color from a picture)

　　本函数选用阈值分割的方法来完成瞳孔定位。根据图像的灰度分布特性，瞳孔部分的灰度值最小，其次为虹膜部分，最大的为巩膜部分。因此在灰度直方图中，第一个大峰值的邻域即是瞳孔所在区域。(Function selects the threshold segmentation method to accomplish the pupil location. Based on image gray distribution features, the pupil part of the grey value of the minimum, followed by the iris part, the biggest part of sclera. So in gray level histogram, the peak value of the first big neighborhood is the pupil area.)