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所在平台: Udemy |
课程主页: https://www.udemy.com/course/computer-vision-masterclass/
课程评论：没有评论
课程名称：计算机视觉精英课程 课程概述：计算机视觉是人工智能的一个子领域，致力于创建能够像人眼一样处理、分析和识别视觉数据的系统。其在多个行业中有广泛的商业应用，如安全、营销、决策制定和生产。例如，智能手机利用计算机视觉通过面部识别解锁设备，自驾车则使用该技术检测行人并与周围车辆保持安全距离，安全摄像头用于识别环境中的人以触发报警。本课程将带您全面了解计算机视觉的世界，学习14种主要计算机视觉技术的逐步实施。如果您之前对计算机视觉并不熟悉，课程结束后，您将对所有领域有一个实践性的概览。 课程内容包括： - 使用OpenCV和Dlib库检测图像和视频中的人脸 - 学习如何训练LBPH算法识别面部，同样使用OpenCV和Dlib库 - 使用KCF和CSRT算法在视频中跟踪对象 - 理解人工神经网络的理论并实现图像分类 - 运用卷积神经网络进行图像分类 - 使用迁移学习和微调提升卷积神经网络的效果 - 使用神经网络检测图像和视频中的情感 - 使用自编码器和TensorFlow压缩图像 - 利用YOLO技术检测物体，这是执行此任务的强大方法之一 - 使用OpenCV识别视频中的手势和动作 - 创造梦幻般的图像，应用Deep Dream技术 - 通过风格转移结合图像风格 - 利用生成对抗网络(GANs)生成现实中不存在的图像 - 通过图像分割提取有用的信息 您将在课程中获得算法的基本直觉，并逐步使用Python语言和Google Colab实现一些项目。
Computer Vision is a subarea of Artificial Intelligence focused on creating systems that can process, analyze and identify visual data in a similar way to the human eye. There are many commercial applications in various departments, such as: security, marketing, decision making and production. Smartphones use Computer Vision to unlock devices using face recognition, self-driving cars use it to detect pedestrians and keep a safe distance from other cars, as well as security cameras use it to identify whether there are people in the environment for the alarm to be triggered. In this course you will learn everything you need to know in order to get in this world. You will learn the step-by-step implementation of the 14 (fourteen) main computer vision techniques. If you have never heard about computer vision, at the end of this course you will have a practical overview of all areas. Below you can see some of the content you will implement:Detect faces in images and videos using OpenCV and Dlib librariesLearn how to train the LBPH algorithm to recognize faces, also using OpenCV and Dlib librariesTrack objects in videos using KCF and CSRT algorithmsLearn the whole theory behind artificial neural networks and implement them to classify imagesImplement convolutional neural networks to classify imagesUse transfer learning and fine tuning to improve the results of convolutional neural networksDetect emotions in images and videos using neural networksCompress images using autoencoders and TensorFlowDetect objects using YOLO, one of the most powerful techniques for this taskRecognize gestures and actions in videos using OpenCVCreate hallucinogenic images using the Deep Dream techniqueCombine style of images using style transferCreate images that don't exist in the real world with GANs (Generative Adversarial Networks)Extract useful information from images using image segmentationYou are going to learn the basic intuition about the algorithms and implement some project step by step using Python language and Google Colab
