PIFu：像素对齐的隐式函数，可实现高分辨率的带披肩的人类数字化 消息： [2020/05/04]添加了用于训练数据生成的EGL渲染选项。 现在，您可以使用无头机器创建自己的训练数据！ [2020/04/13]提供了带有Google Colab的演示（包括可视化）。 特别感谢 ！！！ [2020/02/26]许可证已更新为MIT许可证！ 享受！ 该存储库包含“ ”的pytorch实现。 如果您发现该代码对您的研究有用，请考虑引用该论文。 @InProceedings{saito2019pifu, author = {Saito, Shunsuke and Huang, Zeng and Natsume, Ryota and Morishima, Shigeo and Kanazawa, Angjoo and Li, Hao}, title = {PIFu: Pixel-Ali

背景分离matlab代码计算机视觉与模式识别 内容 1.关于此存储库 该存储库是基本计算机视觉和图像处理技术的集合。 算法的实现在MATLAB中。 2.直方图均衡 图像处理技术。 查看更多详细信息。 代码 3.对比度拉伸 图像处理技术。 查看更多详细信息。 代码 4.边缘检测 图像处理技术。 查看更多详细信息。 代码 5.去除背景 图像处理技术。 查看更多详细信息。 代码 6.背景前景分离 前景检测和背景减法是计算机视觉和图像处理中的主要任务。 查看更多详细信息。 代码

Welcome to the Practitioner Bundle of Deep Learning for Computer Vision with Python! This volume is meant to be the next logical step in your deep learning for computer vision education after completing the Starter Bundle. At this point, you should have a strong understanding of the fundamentals of parameterized learning, neural net works, and Convolutional Neural Networks (CNNs). You should also feel relatively comfortable using the Keras library and the Python programming language to train your own custom deep learning networks. The purpose of the Practitioner Bundle is to build on your knowledge gained from the Starter Bundle and introduce more advanced algorithms, concepts, and tricks of the trade — these tech- niques will be covered in three distinct parts of the book. The first part will focus on methods that are used to boost your classification accuracy in one way or another. One way to increase your classification accuracy is to apply transfer learning methods such as fine-tuning or treating your network as a feature extractor. We’ll also explore ensemble methods (i.e., training multiple networks and combining the results) and how these methods can give you a nice classification boost with little extra effort. Regularization methods such as data augmentation are used to generate additional training data – in nearly all situations, data augmentation improves your model’s ability to generalize. More advanced optimization algorithms such as Adam [1], RMSprop [2], and others can also be used on some datasets to help you obtain lower loss. After we review these techniques, we’ll look at the optimal pathway to apply these methods to ensure you obtain the maximum amount of benefit with the least amount of effort.

剑桥计算机科学笔记 [！[CC BY 4.0] [cc-by-shield]] [cc-by] IB部分的计算机科学修订说明。 ！>这些注释仅用于索引目的。 笔记绝对不能代替官方讲义和笔记。 ！>注释不负责内容的正确性和有效性。 任何错误都可能是我的。 欢迎捐款！ 您可以提交拉取请求或在github存储库上添加问题。

PyCNN：使用Python的细胞神经网络进行图像处理 细胞神经网络（CNN） 是最早在1988年细胞神经网络提出类似于神经网络，与该通信在相邻单元之间只允许的差的平行计算模式。 图像处理是其。 CNN处理器旨在执行图像处理； 具体来说，CNN处理器的原始应用是执行数字处理器无法实现的实时超高帧速率（> 10,000 frame / s）处理。 这个python库是CNN的实现，用于图像处理。 注：该库已在发表的研究被引用，寻找在参考部分中引用＃19。 我很高兴这个图书馆可以为社区提供帮助。 注意：不得将细胞神经网络（CNN）与完全不同的卷积神经网络（ConvNet）混淆。 如上图所示，想象一个带有反馈回路的控制系统。 f（x）是分段线性S型函数。 控制（模板B）和反馈（模板A）模板（系数）是可配置的，并控制系统的输出。 在确定常用图像处理技术的模板方面已经进行了重大研究，这些模板已发布在此。 进一步阅读： 动机 这是2014年第14届细胞纳米级网络和应用（CNNA）大会上演示的扩展。我写了一篇博客文章，可在。 我的论文使用了该库在IEEE Xplore中发布了一个。 依存

Computer Animation Algorithms and Techniques

计算机视觉 算法与应用，这是一本经典的计算机视觉的教程，由Richard Szeliski撰写，本书清晰无无污染，适合打印（ps 这本书是英文版的）

Structure And Interpretation Of Computer Programs 英文无水印pdf pdf所有页面使用FoxitReader和PDF-XChangeViewer测试都可以打开 本资源转载自网络，如有侵权，请联系上传者或csdn删除 本资源转载自网络，如有侵权，请联系上传者或csdn删除

Data structures and tools from computational geometry help to solve problems in computer graphics; these methods have been widely adopted by the computer graphics community yielding elegant and efficient algorithms. This book focuses on algorithms and data structures that have proven to be versatile, efficient, fundamental, and easy to implement. The book familiarizes students, as well as practitioners in the field of computer graphics, with a wide range of data structures. The authors describe each data structure in detail, highlight fundamental properties, and present algorithms based on the data structure. A number of recent representative and useful algorithms from computer graphics are described in detail, illuminating the utilization of the data structure in a creative way. 花了很大力气才找到的，侵权删

Multiple View Geometry in Computer Vision( 计算机视觉中的多视图几何 )中英文文档各一份 hartley 大神之作