面部对齐 通过回归树进行人脸对齐 预要求 Visual Studio 2012+ 和 OpenCV 安装在 C:/opencv

计算机网络：自顶向下方法第8版，Global版

Hands-On Computer Vision with Julia is a thorough guide for developers who want to get started with building computer vision applications using Julia. Julia is well suited to image processing because it's easy to use and lets you write easy-to-compile and efficient machine code. This book begins by introducing you to Julia's image processing libraries such as Images.jl and ImageCore.jl. You'll get to grips with analyzing and transforming images using JuliaImages; some of the techniques discussed include enhancing and adjusting images. As you make your way through the chapters, you'll learn how to classify images, cluster them, and apply neural networks to solve computer vision problems. In the concluding chapters, you will explore OpenCV applications to perform real-time computer vision analysis, for example, face detection and object tracking. You will also understand Julia's interaction with Tesseract to perform optical character recognition and build an application that brings together all the techniques we introduced previously to consolidate the concepts learned. By end of the book, you will have understood how to utilize various Julia packages and a few open source libraries such as Tesseract and OpenCV to solve computer vision problems with ease. What you will learn Analyze image metadata and identify critical data using JuliaImages Apply filters and improve image quality and color schemes Extract 2D features for image comparison using JuliaFeatures Cluster and classify images with KNN/SVM machine learning algorithms Recognize text in an image using the Tesseract library Use OpenCV to recognize specific objects or faces in images and videos Build neural network and classify images with MXNet Who This Book Is For Hands-On Computer Vision with Julia is for Julia developers who are interested in learning how to perform image processing and want to explore the field of computer vision. Basic knowledge of Julia will help you understand the concepts more effectively.

计算机图形学：计算机图形学算法实现

*从头开始=相当多的东西-使用glm不必实现基本的数学运算 路径追踪器 抗锯齿，阴影柔和等（免费） 胃肠道 适用于反射（镜），反射/折射（玻璃），固体材料的BRDF。 对象（可以使用任何BRDF） 多雾路段 平行性 光栅化器 对象 贴图 FXAA 阴影贴图 剪裁

A good book concernning metamath.

计算机组成原理 硬件与接口 答案 Computer.Organization.and.Design The.Hardware.Software.Interface Manual

计算机组成教材，《计算机组成与设计：软硬件接口》英文版第三版

信息网 通过提出的MIRNet架构的Tensorflow实现，。 Lanuch笔记本： Wandb日志： ://wandb.ai/19soumik-rakshit96/mirnet MIRNet的TFLite变体： : 。 Tensorflow Hub上的TFLite模型： ://tfhub.dev/sayakpaul/lite-model/mirnet-fixed/dr/1 。 MIRNet的Tensorflow JS变体： : 。 预先训练的体重 在128x128补丁程序上进行了训练： ：//drive.google.com/file/d/1sUlRD5MTRKKGxtqyYDpTv7T3jOW6aVAL/view usp = sharing 已针对256x256补丁进行了培训： https ：//drive.google.com/file/d/1sUlRD5MTR

图像重复数据删除器（imagededup） imagededup是一个python软件包，它简化了在图像集合中查找精确且几乎重复的任务。 该软件包提供了利用散列算法的功能，这些算法特别擅长查找精确的重复项，而卷积神经网络也擅长查找近似的重复项。 还提供了评估框架来判断给定数据集的重复数据删除质量。 以下详细说明了软件包提供的功能： 使用以下算法之一在目录中查找重复项： （CNN） （PHash） （DHash） 波哈希（WHash） （AHash） 使用上述算法之一生成图像编码。 给定基本事实映射的框架来评估重复数据删除的有效性。 绘制找到给定图像文件的重复项。 该软