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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/introduction-computer-vision-watson-opencv
课程评论：没有评论
课程名称：计算机视觉与图像处理导论 课程概述： 计算机视觉是机器学习和人工智能中最激动人心的领域之一，广泛应用于自动驾驶汽车、机器人技术、增强现实等多个行业。本课程适合初学者，将帮助您理解计算机视觉及其在各行业中的多种应用。 在本课程中，您将使用Python、Pillow和OpenCV进行基本的图像处理，并执行图像分类和物体检测。课程包含多个实验室和练习，结合Jupyter Labs和计算机视觉学习工作室（CV Studio），后者是一种免费的计算机视觉学习工具，能够让您上传、训练和测试自定义的图像分类器和检测模型。课程结束时，您将创建自己的计算机视觉网络应用并将其部署到云端。 本课程不需要任何机器学习或计算机视觉的前期经验，但需要具备一定的Python编程知识和高中数学基础。 课程大纲： 1. **计算机视觉导论**：讨论图像处理这一快速发展的领域及其广泛应用，从智能手机图像清晰度到帮助医生治愈疾病。 2. **OpenCV和Pillow的图像处理**：学习使用Python库OpenCV和Pillow进行图像处理的基础知识，以增强图像或提取有效信息。 3. **机器学习图像分类**：了解计算机视觉中常用的不同机器学习分类方法，包括K近邻、逻辑回归、SoftMax回归和支持向量机，并学习图像特征。 4. **图像分类的神经网络与深度学习**：学习关于神经网络、全连接神经网络和卷积神经网络（CNN）及其层、激活函数（如ReLU）和不同的CNN架构（如ResNet和LenNet）。 5. **目标检测**：学习使用不同方法进行物体检测，包括Haar Cascade分类器、R-CNN和MobileNet。 6. **项目案例：并非真正的自动驾驶汽车 - 交通标志分类**：最后一周，您将构建一个计算机视觉应用并通过云端的Code Engine进行部署。您将创建一个自定义分类器，并在自己的图像上进行训练和测试。 该课程通过实用的训练，帮助您在计算机视觉领域奠定坚实的基础。
Name:Introduction to Computer Vision
Description:In this module, we will discuss the rapidly developing field of image processing. In addition to being the first step in Computer Vision, it has broad applications ranging anywhere from making your smartphone's image look crystal clear to helping doctors cure diseases.
Name:Image Processing with OpenCV and Pillow
Description:Image processing enhances images or extracts useful information from the image. In this module, we will learn the basics of image processing with Python libraries OpenCV and Pillow.
Name:Machine Learning Image Classification
Description:In this module, you will Learn About the different Machine learning classification Methods commonly used for Computer vision, including k nearest neighbours, Logistic regression, SoftMax Regression and Support Vector Machines. Finally, you will learn about Image features.
Name:Neural Networks and Deep Learning for Image Classification
Description:In this module, you will learn about Neural Networks, fully connected Neural Networks, and Convolutional Neural Network (CNN). You will learn about different components such as Layers and different types of activation functions such as ReLU. You also get to know the different CNN Architecture such as ResNet and LenNet.
Name:Object Detection
Description:In this module, you will learn about object detection with different methods. The first approach is using the Haar Cascade classifier, the second one is to use R-CNN and MobileNet.
Name:Project Case: Not Quite a Self-Driving Car - Traffic Sign Classification
Description:In the final week of this course, you will build a computer vision app that you will deploy on the cloud through Code Engine. For the project, you will create a custom classifier, train it and test it on your own images.
Computer Vision is one of the most exciting fields in Machine Learning and AI. It has applications in many industries, such as self-driving cars, robotics, augmented reality, and much more. In this beginner-friendly course, you will understand computer vision and learn about its various applications across many industries. As part of this course, you will utilize Python, Pillow, and OpenCV for basic image processing and perform image classification and object detection. This is a hands-on course and involves several labs and exercises. Labs will combine Jupyter Labs and Computer Vision Learning Studio (CV Studio), a free learning tool for computer vision. CV Studio allows you to upload, train, and test your own custom image classifier and detection models. At the end of the course, you will create your own computer vision web app and deploy it to the Cloud. This course does not require any prior Machine Learning or Computer Vision experience. However, some knowledge of the Python programming language and high school math is necessary.
