Introduction to Computer Vision and Image Processing

Go to Course: https://www.coursera.org/learn/introduction-computer-vision-watson-opencv

Introduction

### Course Review: Introduction to Computer Vision and Image Processing In the ever-evolving landscape of technology, computer vision stands out as a vital component of various applications, including self-driving cars, robotics, augmented reality, and more. "Introduction to Computer Vision and Image Processing" on Coursera offers an invaluable foundation for anyone eager to explore this fascinating field. Here's a detailed review of the course, highlighting its key features, syllabus, and my recommendations. #### Course Overview This beginner-friendly course demystifies the concepts of computer vision while providing hands-on experience using Python, Pillow, and OpenCV tools. The course is structured to ensure that even those with limited programming knowledge can grasp the essentials of image processing and its applications across various industries. From enhancing smartphone images to aiding doctors in diagnostic processes, this course proves that computer vision techniques have far-reaching and transformative applications. #### Syllabus Breakdown 1. **Introduction to Computer Vision** - The journey begins with an overview of computer vision as a rapidly developing field. This module establishes a foundational understanding of the significance and applications of image processing, setting the stage for deeper exploration. 2. **Image Processing with OpenCV and Pillow** - Students will delve into the practical aspects of image processing using popular Python libraries. The introduction of tools like OpenCV and Pillow brings theory to life, allowing learners to see the immediate effects of code on images. 3. **Machine Learning Image Classification** - This module introduces various machine learning classification methods used in computer vision. Students learn key models such as k-nearest neighbors, logistic regression, and support vector machines, along with the important concept of image feature extraction. 4. **Neural Networks and Deep Learning for Image Classification** - Building on the previous lessons, this section explores how neural networks function, emphasizing convolutional neural networks (CNNs). Key concepts are broken down into manageable segments, including layers, activation functions like ReLU, and popular architectures like ResNet and LenNet. 5. **Object Detection** - Students engage with object detection methods. The module covers techniques including the Haar Cascade classifier and advanced methods like R-CNN and MobileNet, allowing learners to understand how systems can identify and classify objects within images. 6. **Project Case: Not Quite a Self-Driving Car - Traffic Sign Classification** - The course culminates in an exciting hands-on project where students build a computer vision application that they can deploy to the cloud. This final project ensures that learners can apply their acquired knowledge practically, further enhancing their learning experience. #### Recommendation I highly recommend the "Introduction to Computer Vision and Image Processing" course for anyone interested in entering the tech fields of machine learning and artificial intelligence. Here’s why: - **Hands-On Learning**: The course prioritizes practical application, which helps reinforce learning through real-world projects and coding exercises. - **Accessibility**: The course is designed for beginners, making it suitable for individuals with little to no prior experience in programming or computer vision. - **Comprehensive Coverage**: From the basics of image processing to advanced techniques involving neural networks and object detection, the syllabus is thorough and well-structured. - **Real-World Applications**: Understanding how computer vision can transform various industries makes the learning relevant and inspiring. For anyone serious about pursuing a career in data science, artificial intelligence, or software development, this course is an excellent starting point to build essential skills and knowledge in computer vision. Enroll today and unlock your potential in one of the most significant fields in technology!

Syllabus

Introduction to Computer Vision

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.

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.

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.

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.

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.

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.