extract

首先对车牌识别系统的现状和已有的技术进行深入的研究，然后开发出一个基于 Python 的车牌识别系统，文中先对车辆图像进行高斯去噪、灰度化和边缘检测等预处理方法，然后用颜色特征和形态特征相结合的方法来确定车牌位置，用彩色分割法来完成车牌分割，最后，运用 SVM 分类训练器完成字符识别并使用Python 软件环境进行车牌识别的仿真实验。(License plate recognition based on SVM)

非均匀有理B样条 第2版.pdf NURBS理论经典书籍(The nurbs book(second edition) NURBS Theory Classic Books)

光线跟踪算法，能显示基本图形（球形，立方体，墙面），实现阴影，高光，反射，折射等效果(Ray tracing algorithm, can display basic graphics (sphere, cube, wall), to achieve shadows, light, reflection, refraction and other effects)

说明：  三次B样条曲线，计算机图形学实验中的一个小程序，很有意思哦(Three B-spline curves, Computer Graphics in a small experimental procedure, very interesting oh)

对图像的隐写术，进行信息隐藏，版权保护。(Steganography is used to hide information and copyright protection.)

存取图像字段 数字图像的存取 将图像转化为数字信息(Access image field )

针对电气设备同一场景的红外与可见光图像间一致特征难以提取和匹配的问题，提出了一种基于斜率一致性的配准方法。首先通过数学形态学方法分别提取红外与可见光图像的边缘，得到粗边缘图像；然后通过SURF算法提取两幅边缘图像的特征点，根据正确的匹配点对之间斜率一致性的先验知识，进行特征点匹配；最后通过最小二乘法求得仿射变换模型参数并实现两幅图像的配准。实验结果表明，该方法有效提高了匹配点对的正确率，能够对电气设备红外和可见光图像实现高精度的配准。(In this paper, a method based on slope consistency is proposed to solve the problem that the uniform features of infrared and visible images are difficult to extract and match. Firstly, the edges of infrared and visible images were extracted by mathematical morphology, and the rough edge images were obtained. Then, the feature points of two edge images were extracted by SURF algorithm, and the priori knowledge of slope consistency was obtained according to the correct matching point, and the feature points were matched. Finally, the model parameters of affine transformation are obtained by least square method and the registration of two images is realized. The experimental results show that this method can effectively improve the accuracy of matching points, and can realize high precision registration of infrared and visible images of electrical equipment.)

主要包括3种去噪方法，Bayesian Least Squares，BM3d 和Non-local means (this attachment contains three classical denoising methods Bayesian Least Squares，BM3d and Non-local means )

说明：  成功实现了车牌的定位、字符分割、并用神经网络实现了字符识别功能。可运行。(The successful implementation of the license plate of the positioning, character segmentation, and neural network achieved a character recognition function. Run.)

对监控视频中的目标进行识别，提取背景信息(The target in the surveillance video is identified and background information is extracted)