How to learn machine vision?

Learning machine vision is a complex task, involving image processing, pattern recognition and machine learning. Here are some steps and suggestions to help you start learning machine vision:

1. Establish the foundation of mathematics and programming: Machine vision needs a certain mathematical foundation, such as linear algebra, probability theory, statistics, etc. In addition, familiarity with programming languages (such as Python) and related libraries (such as OpenCV) is also necessary.

2. Learn the basic knowledge of image processing: understand common image processing technologies, such as filtering, edge detection, feature extraction, etc. These technologies are the basis of constructing machine vision algorithms.

3. Learning machine learning and deep learning: Master the basic concepts and algorithms of machine learning and deep learning, such as support vector machine (SVM) and convolutional neural network (CNN). These methods are widely used in machine vision, such as object detection and image classification.

4. Explore open source tools and libraries: Use open source tools and libraries to speed up the learning process. For example, OpenCV provides rich image processing functions; TensorFlow and PyTorch are popular deep learning frameworks that provide powerful image processing and machine learning functions.

5. Complete the practical project: consolidate the learned knowledge through the practical project. We can start with simple image processing tasks and gradually challenge more complex problems, such as face recognition and target detection.

6. Participate in competitions and clubs: participate in machine vision competitions (such as Kaggle) or join relevant clubs, exchange experiences and share resources with other learners, and constantly improve themselves through practice.

7. Keep learning and keep up with the latest progress: Machine vision is a rapidly developing field, and new algorithms and technologies emerge one after another. Keep learning attitude and pay attention to the latest research results and industry trends.

Please note that machine vision is a broad and in-depth field, which needs long-term study and practice to master. Therefore, I suggest you continue to invest time and energy in integrating theory with practice in order to better master machine vision technology.