本课程深入探讨了视觉人工智能领域的前沿算法与核心原理，并通过20个精心设计的项目，将抽象的理论知识转化为实际操作技能。学员将一步步学习如何应用这些先进的视觉AI技术，从图像识别到物体检测，再到复杂的场景理解，每一个项目都旨在加深理解并锻炼实践能力。本课程适合对AI视觉领域有兴趣的学生、工程师及研究人员。
随着人工智能技术的飞速发展，视觉AI已成为其最令人瞩目的分支之一。本课程旨在为有志于深入了解视觉AI的学习者提供全面的理论知识和丰富的实战经验。我们将学习多种流行且实用的算法，包括卷积神经网络（CNN）、生成对抗网络（GAN）以及强化学习等，并将这些算法应用于真实世界问题的解决。
通过20个精选项目，学员不仅能够掌握视觉AI的基本概念和算法，还能通过项目实践学会如何处理实际问题，例如面部识别、情感分析、自动驾驶车辆的视觉系统等。每个项目都配备了详细的步骤说明和必要的理论背景，确保学员能在实践中深化理解。
无论你是初学者还是希望扩展知识面的专业人士，这个课程都将帮助你构建坚实的视觉AI知识基础，并提升解决复杂视觉任务的能力。
This course delves into the cutting-edge algorithms and foundational principles of vision artificial intelligence and transforms abstract theoretical knowledge into practical skills through 20 carefully crafted projects. Participants will learn step by step how to apply these advanced vision AI technologies, from image recognition to object detection and complex scene understanding. Each project is designed to deepen understanding and hone practical skills. This course is suitable for students, engineers, and researchers interested in the field of AI vision.
As artificial intelligence technology develops at an unprecedented pace, vision AI has emerged as one of its most fascinating branches. This course is designed to offer learners aspiring to delve deeper into the field of vision AI a comprehensive theoretical framework coupled with rich practical experience. We will study a variety of popular and practical algorithms, such as Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), and reinforcement learning, applying these to solve real-world problems.
With 20 selected projects, participants will not only master the basic concepts and algorithms of vision AI but will also learn how to tackle actual issues through project practice, such as facial recognition, sentiment analysis, and the vision systems of autonomous vehicles. Each project comes with detailed step-by-step instructions and necessary theoretical background to ensure that students can deepen their understanding through practice.
Whether you are a beginner or a professional looking to expand your knowledge, this course will help you build a solid foundation in vision AI knowledge and enhance your ability to solve complex visual tasks.
- 快速搞定 Python 库和环境
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- 下载安装 Conda 软件
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- 创建 Python 虚拟环境
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- Conda/pip 安装第三方包
- 国内加速镜像
- 安装 OpenCV
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- 运行 Demo 程序
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- Numpy 和图像基础
- Jupyter Lab
- Numpy 基础用法
- 图像的本质
- 用 OpenCV 玩转图像和视频
- 为什么学习 OpenCV？
- VScode 切换到 Conda 环境实现代码自动补全
- OpenCV 读取、缩放、翻转、写入图像
- OpenCV 在图像上绘制文字、几何图形
- OpenCV 视频操作
- OpenCV 连接 webcam 或 USB 摄像头
- OpenCV 操作视频文件
- OpenCV 在视频上添加文字、图形
- 实战项目 1：AI 手势虚拟拖拽方块
- 实战项目 2：毛笔书体检测与识别目标
- 形态学变换（morphological）
- Canny边缘检测算法
- 检测书法文字
- 图片分类（HOG+SVM）
- HOG：方向梯度直方图
- 计算梯度直方图
- Block 归一化
- SVM（支持向量机）
- 代码实现
- 代码实现
- 实战项目 3：人脸考勤机
- 人脸检测方法概述
- Haar特征
- haar代码实现
- HOG代码实现
- 卷积神经网络CNN方法
- SSD代码实现
- MTCNN代码实现
- 几种算法的对比
- 视频流检测人脸
- 人脸识别的方法
- EigenFaces（特征脸，1991年）
- FisherFaces（1997年）
- LBPH（Local Binary Pattern Histogram，1994年）
- 代码实现