matlab sift特征提取代码 Scene-Recognition-with-Bag-of-Words（基于词袋模型的场景识别） 1 实验目的 使用了两种特征提取算法（Tiny images feature和Bag of sift）及两种分类算法（k-Nearest Neighbor和SVM）进行场景识别。 Tiny + Nearest Neighbor Tiny + SVM Bags of SIFT + Nearest Neighbor Bags of SIFT+SVM 2 代码结构与功能 主函数：project3.m Tiny images feature 特征提取：get_tiny_images.m Bag of SIFT特征提取： build_vocabulary.m 实现词袋中标准词汇的选择 get_bags_of_sifts.m 实现词袋模型的构建 k-Nearest Neighbor分类器：nearest_neighbor_classify.m SVM分类器：svm_classify.m 获取图片路径：get_image_paths.m 将结果呈现成webpage形式

face_recognition_py 本项目基于OpenCV使用Haar级联分类器实现人脸检测，与dlib库进行实时跟踪，应用LBPH算法开发了一个功能相对完整的人脸识别系统。系统采用MySQL进行数据存储，能够进行学生上课考勤人脸点名的功能，并拥有基于PyQt5设计的GUI实现。 系统预览 核心框架 人脸采集 数据管理 如何运行？ 以下操作基于Anaconda3环境，并在Windows10 x64上测试。 克隆代码 $ git clone https://github.com/kuronekonano/Face-Recognition-Based-Attendance-System.git $ cd Face-Recognition-Based-Attendance-System 创建Python虚拟环境 $ conda create -n opencv python=3.6 $ ac

matlab掌纹识别代码掌纹识别个人身份 该存储库包含一个 MATLAB 程序，可通过识别掌纹来识别系统的真实用户。 用于此识别的图像取自 PolyU Palmprint 数据库。 该项目是论文的实现：Y. Xu、L. Fei 和 D. Zhang，“组合左右掌纹图像以实现更准确的个人识别”，在 IEEE 图像处理交易，卷。 24，没有。 2，第 549-559 页，2015 年 2 月。数据集、论文和代码可在此存储库中找到。

GMM_Digital_Voice_Recognition 基于GMM与MFCC特征进行数字0-9的语音识别，GMM，MFCC，语音识别，中文数据，sklearn，scikit-learn，数字语音识别。 预安装 conda create -n GMM -c anaconda python=3.6 numpy pyaudio scipy #也可以使用pip conda activate GMM pip install -r requirements.txt 数据链接： ://pan.baidu.com/s/124TiAs8m7Ioa2_3dUrxGSg提取码：xsfe 以下命令假设下载

可能是全站最完善的基于模板匹配的车牌识别系统了，经过我们的不断改善，只需要略微改变一下路径以及增添些许模板就可以变成你自己的代码，应付课程设计完全没有问题。

尽管Bishop的行文很优美，有时候简直可以拿出来当speech背诵，但是对于理论的理解还是母语比较稳妥和踏实。

使用BPNN的人脸识别。 包含1.使用Matlab的反向传播神经网络（自定义代码）代码进行人脸识别。 2.使用Matlab的反向传播网络（内置）代码进行人脸识别。该项目目前已关闭，Adeel Raza Azeemi

跟着导师学习，导师让我看行为识别相关的顶会论文.这是我自己做的PPT，内容详细，风格简洁

The dramatic growth in practical applications for machine learning over the last ten years has been accompanied by many important developments in the underlying algorithms and techniques. For example, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic techniques. The practical applicability of Bayesian methods has been greatly enhanced by the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation, while new models based on kernels have had a significant impact on both algorithms and applications. This completely new textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory. The book is suitable for courses on machine learning, statistics, computer science, signal processing, computer vision, data mining, and bioinformatics. Extensive support is provided for course instructors, including more than 400 exercises, graded according to difficulty. Example solutions for a subset of the exercises are available from the book web site, while solutions for the remainder can be obtained by instructors from the publisher. The book is supported by a great deal of additional material, and the reader is encouraged to visit the book web site for the latest information.

实时面部表情识别 使用Tensorflow-Keras API中的转移学习（计算机视觉）进行面部表情识别或面部表情识别