ieee_ssr_bench

根据曲率求图像分割的方法，可以在matlab下运行。(According to the curvature of the methods for image segmentation, you can run.)

自己编写的C++小程序，十字算法，用到KD树，能够有效进行点云去噪，压缩数据(I have written a small program C++, cross algorithm used in KD tree, the point cloud can be effectively de-noising, compression of data)

猜首府 反复提示用户输入一个州的首府，并检测是否正确(Guess the capital repeatedly prompts the user for the capital of a state, and to detect the correct)

用来识别火焰边缘，对高速摄影拍摄的火焰图片边缘进行提取(To identify the edge of a flame)

一幅含有噪声的SAR 图像,可用于道路检测,或者图像去噪的研究.图像内容相对丰富清晰.(A SAR image with noise, can be used for road detection, or image denoising studies. Relative abundance of clear image content.)

usps数据库为标准手写数据库，此为mat数据文件，含训练集和测试(USPS database is a standard handwritten database, which is a mat data file, including training set and test)

说明：  机器学习专门研究计算机怎样模拟或实现人类的学习行为，以获取新的知识或技 能，为包括人脸识别领域的人工智能的发展做出了极大的贡献。本文探索如何应用机器学习中的一些技术，使计算机更好地完成人脸识别领域中的人脸检测和人脸验证。 在人脸检测方面，针对如何快速、准确地检出人脸的问题，基于DLIB中特征模型，可以快速提取检测出人脸，并且提取出人脸特征点,主要作用就是快速检测定位人脸。在训练过程中，引入了ResNet机器学习算法，该算法采用多层卷积神经网络结构，对人脸进行多层特征提取和描述，得到人脸特征描述符。通过测试可以证实了上述方法能够检测和识别人脸。(In terms of face detection, for the problem of how to detect faces quickly and accurately, based on the feature model in DLIB, you can quickly extract and detect faces, and extract face feature points, the main role is to quickly detect and locate faces. In the training process, the VGG-FACE machine learning algorithm is introduced. This algorithm uses a multi-layer convolutional neural network structure to perform multi-layer feature extraction and description on the face to obtain the face feature descriptor. Tests can confirm that the above method can detect and recognize human faces.)

说明：  基于python语言的三种图像简单去噪算法(Three simple image denoising algorithms based on Python)

说明：  基于卷积神经网络的图像融合方法，首次将卷积神经网络应用到图像融合领域。(The image fusion method based on convolutional neural network is first applied to image fusion.)

加州理工经典的摄像机标定程序，张正友博士的文章算法(The camera calibration program of the California Institute of Technology, the article algorithm of Dr. Zhang Zhengyou)