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via Udemy |
Go to Course: https://www.udemy.com/course/learn-computer-vision-with-opencv-and-python/
Certainly! Here's a detailed review and recommendation for the Coursera course on Computer Vision and Image Processing: --- **Course Review and Recommendation: Mastering Computer Vision with OpenCV on Coursera** If you're looking to dive into the fascinating world of computer vision and image processing, this Coursera course is an excellent choice for learners of all levels, especially those eager to learn from scratch. What sets this course apart is its comprehensive and practical approach, focusing on real-world applications rather than only theoretical concepts. **Course Content and Structure** The course covers a broad spectrum of topics essential for understanding and implementing computer vision tasks. Starting with foundational concepts such as histogram equalization, thresholding, edge detection, and morphological operations, the course gradually introduces more advanced subjects like keypoint matching, image segmentation, and object detection. One of the most impressive aspects is the inclusion of many real-world examples that go beyond just using built-in OpenCV functions. The instructor commits to expanding the course content with new, special examples, exciting features like an image search app for team logos, and applications for missing and abandoned object detection. New chapters and sections, such as "How to Prepare Dataset and Train Your Deep Learning Model" and "Facial Landmarks and Special Applications," provide crucial practical skills for aspiring computer vision engineers. **Hands-On and Practical Focus** This course emphasizes implementation over mathematical theory, making complex topics accessible by focusing on code and real-world scenarios. Using Python with OpenCV makes it easier to grasp programming concepts quickly without getting bogged down by complex syntax. Learners also benefit from a wide range of projects, including face and eye detection, pedestrian detection, object tracking, and even mini-games that make learning engaging. **Special Features and Updates** The addition of special applications like "Search Team Logos," "Missing and Abandoned Object Detection," and real-time sleep and smile detection demonstrates the course's commitment to providing current and industry-relevant skills. The instructor promises continuous updates, which means you'll learn the latest techniques and watch examples such as soccer player detection and deep learning-based object detection APIs. **Pros:** - Beginner-friendly explanations with minimal heavy math - Extensive practical examples and projects - Step-by-step learning pathway from scratch - Regular updates with new chapters and special applications - Use of open-source tools (OpenCV + Python), making it accessible - Active Q&A and community support **Cons:** - Some learners might find the pace too fast if they lack Python basics - Advanced topics may require additional resources for deeper understanding **Conclusion and Recommendation** This course is highly recommended for anyone interested in starting or advancing their skills in computer vision. It offers a perfect balance between fundamental concepts and cutting-edge applications, making it suitable for students, hobbyists, and industry professionals alike. Whether you aim to develop intelligent applications like facial recognition, object tracking, or custom Deep Learning models, this course provides the necessary tools and knowledge to succeed. The instructor’s clear explanations, combined with real-world examples and continuous enhancements, ensure you stay engaged and well-prepared for practical challenges. --- **Final Verdict:** Enrolling in this course will equip you with practical skills and a solid understanding of computer vision, empowering you to build innovative applications in this rapidly evolving field. Don’t miss out on the opportunity to learn from a well-structured, continuously evolving course supported by a vibrant community! --- Feel free to ask if you'd like a brief summary or specific details!
Note: You will find real world examples (not only using implemented functions in OpenCV) and i'll add more by the time. It means that course content will expand with new special examples!. ***New Chapter***: "How to Prepare dataset and Train Your Deep Learning Model" was added to the course. You will learn how to prepare a simple dataset, label the objects and train your own deep learning model.***New Special App***: "Search team logos" was added to the course. You will learn how you can compare images and find similar image/object in your dataset.***New Chapter***: "Special Apps - Missing and Abandoned Object Detection" was added to the course. You will learn how to do an application for missing object detection and abandoned object detection***New Chapter***: Facial Landmarks and Special Applications (real time sleep and smile detection) videos was added to the course!***Different Special Applications Chapter***: new videos in different topics will be shared under this chapter. You can look at "Soccer players detection" and "deep learning based API for object detection" examples. In this course, you are going to learn computer vision & image processing from scratch. You will reach all resources, have many examples and explanations of these examples.The explanations are easy to understand and also you can ask the points you need.I have shared key concepts with you without the heavily mathematical theory, so we can focus the implementation.Maybe you can find some other resources, videos or blogs to learn about some of these topics explained in my course, but the advantage of this course is that, you will learn computer vision from scratch by following an order, so that you will not loss yourself between many different sources.You will also find many special examples beside the fundamental topics.I preferred to use OpenCV which is an open source computer vision library used and supported by many people!. I have used OpenCV with Python, because Python allows us to focus on the problem easily without spending time for programming syntax/complex codes.I wish this course to be useful for you to learn computer vision, and Actively we can use 'questions and answers' area to share information...You will learn the topics:The key concepts of computer Vision & OpenCVBasic operations: histogram equalization,thresholding, convolution, edge detection, sharpening ,morphological operations, image pyramids.Keypoints and keypoint matchingSpecial App: mini game by using key pointsImage segmentation: segmentation and contours, contour properties, line detection, circle detection, blob detection, watershed segmentation.Special App: People counter Object tracking:Tracking APIs, Filtering by Color.Special App: Tracking of moving object Object detection: haarcascade face and eye detection, HOG pedestrian detectionObject detection with Deep LearningExtra Chapter: How to Prepare dataset and Train Your Deep Learning ModelExtra Chapter: Special Apps - Missing and Abandoned Object DetectionExtra Chapter: Facial Landmarks and Special Applications (real time sleep and smile detection)Extra Chapter: Different Special Applications ( will be updated with special examples in different topics )