Familiar with basic mathematics knowledge
Machine vision involves many mathematical principles and algorithms, such as matrix operation, vector, projection geometry, optimization algorithm and so on. Therefore, before learning machine vision, it is suggested to master basic mathematical knowledge such as linear algebra, calculus and probability theory.
Learning programming languages
C#, C++ and VB.net are all commonly used languages in the field of machine vision, so it is recommended to learn at least one of them. You can learn programming languages through online tutorials, books or attending training courses.
Learn the basics of machine vision.
Learning the basic knowledge of machine vision includes image processing, feature extraction, image matching, object detection and recognition. These basic knowledge can be obtained by studying related textbooks, attending training courses or self-study.
Master the operation methods of VisionPro, Halcon and OpenCV software.
It is very important to choose the appropriate machine vision software library for different application scenarios. It is recommended to spend time mastering the use of VisionPro, Halcon and OpenCV. You can refer to official documents, books or online tutorials for learning.
Practice and debug code: Through my own practice, I deeply understand and master image processing and machine vision algorithms. Start with simple tasks, such as image classification and target detection. Step by step, master the algorithm and model through practice and debug the code to solve the problem.