人脸识别 该存储库包含使用带有tkinter gui界面的openCV和python进行面部识别的代码。 如果要测试代码，请运行train.py文件 使用的技术：-openCV（开源计算机视觉）-Python -tkinter GUI界面 在这里，我正在使用OpenCV（Python）开发基于面部识别的考勤管理系统。 只需面对照相机，即可标记出他们的出席情况。 怎么运行的 ： 当我们运行train.py时，将打开一个窗口，询问输入ID和输入名称。 输入名称和ID后，我们必须单击“拍摄图像”按钮。 通过单击“拍摄图像”，将打开正在运行的计算机的摄像头，并开始拍摄人的图像样本。此ID和名称存储在StudentDetails文件夹中，文件名为StudentDetails.csv。 它以60张图像为样本并将其存储在TrainingImage文件夹中。完成后它会通知iamges已保存。 采集图像

本文讲叙了如何根据采集来的肌电信号进行特征计算，并进行了不同种方法的计算和求值

用python3+opencv3做的中国车牌识别，包括算法和客户端界面，只有2个文件，surface.py是界面代码，predict.py是算法代码，界面不是重点所以用tkinter写得很简单。 ### 使用方法： 版本：python3.4.4，opencv3.4和numpy1.14和PIL5 下载源码，并安装python、numpy、opencv的python版、PIL，运行surface.py即可 ### 算法实现： 算法思想来自于网上资源，先使用图像边缘和车牌颜色定位车牌，再识别字符。车牌定位在predict方法中，为说明清楚，完成代码和测试后，加了很多注释，请参看源码。车牌字符识别也在predict方法中，请参看源码中的注释，需要说明的

UCF101 is provided by University of Central Florida.本数据集由中央佛罗里达大学提供。 UCF101_TrainTestSplits-DetectionTask_datasets.zip UCF101_TrainTestSplits-RecognitionTask_datasets.zip

Domain adaptation is an active, emerging research area that attempts to address the changes in data distribution across training and testing datasets. With the availability of a multitude of image acquisition sen- sors, variations due to illumination, and viewpoint among others, com- puter vision applications present a very natural test bed for evaluating domain adaptation methods. In this monograph, we provide a compre- hensive overview of domain adaptation solutions for visual recognition problems. By starting with the problem description and illustrations, we discuss three adaptation scenarios namely, (i) unsupervised adap- tation where the “source domain” training data is partially labeled and the “target domain” test data is unlabeled, (ii) semi-supervised adaptation where the target domain also has partial labels, and (iii) multi-domain heterogeneous adaptation which studies the previous two settings with the source and/or target having more than one domain, and accounts for cases where the features used to represent the data in each domain are different. For all these topics we discuss existing adaptation techniques in the literature, which are motivated by the principles of max-margin discriminative learning, manifold learning, sparse coding, as well as low-rank representations. These techniques have shown improved performance on a variety of applications such as object recognition, face recognition, activity analysis, concept clas- sification, and person detection. We then conclude by analyzing the challenges posed by the realm of “big visual data”, in terms of the generalization ability of adaptation algorithms to unconstrained data acquisition as well as issues related to their computational tractability, and draw parallels with the efforts from vision community on image transformation models, and invariant descriptors so as to facilitate im- proved understanding of vision problems under uncertainty.

该代码实现了用户组的操作，以及对用户人脸的添加，删除操作，有兴趣的朋友，请自行下载。

本资源包含Pattern Recognition And Machine Learning（Bishop编写）的英文版、马春鹏翻译的中文版、章节习题答案、张兆翔老师的PPT、书中的所有图。感谢无私公开这些资料的作者。

压缩包文件有： 1.PATTERN RECOGNITION AND MACHINE LEARNING CHRISTOPHER M.BISHOP 2.SOLUTIONS TO EXERCISES WEB-EDITION(答案)

Deep_Learning_for_Distant_Speech_Recognition Mirco Ravanelli

Deep_Learning_for_Hand_Gesture_Recognition_on_Skeletal_Data Guillaume Devineau