If you have no prior experience with machine vision and want to learn it, you might have your first question:
Is machine vision really that difficult to learn?
Before answering this question, let's first understand what machine vision is.
I. Definition of Machine Vision
Machine vision is the use of machines to replace human eyes for measurement and judgment.
A machine vision system refers to the process of converting a captured target into an image signal using machine vision products (i.e., image capture devices, which are divided into CMOS and CCD), and then transmitting it to a dedicated image processing system to obtain shape information of the target based on pixel distribution.
Information such as brightness and color is converted into digital signals; the imaging system performs various operations on these signals to extract the features of the target, and then controls the actions of the device based on the identification results.
II. Machine Vision Principles
Machine vision inspection systems use CCD cameras to convert detected targets into image signals, which are then transmitted to a dedicated image processing system. Based on pixel distribution and information such as brightness and color, these signals are converted into digital signals. The image processing system performs various operations to extract the features of the target, such as area, quantity, position, and length. Then, based on preset permissibility and other conditions, including size, output results, angle, quantity, pass/fail, presence/absence, etc., automatic recognition is achieved.
III. Applications of Machine Vision
Machine vision applications mainly include two aspects: inspection and robot vision.
1. Testing:
It can also be divided into high-precision quantitative detection (such as cell classification in micrographs, size and position measurement of mechanical parts) and qualitative or semi-quantitative detection without measuring equipment (such as visual inspection, product and component identification and positioning on the assembly line), defect detection and assembly integrity testing.
2. Robot Vision:
Used to guide the robot's actions and operations in a variety of ranges, such as picking up a workpiece from a jumbled pile of material sent from a hopper and placing it in a certain direction on a conveyor belt or other equipment (i.e., the hopper picking problem).
For operations and movements within a small range, tactile sensing technology must also be used.
In addition, there are applications such as automatic optical inspection, face recognition, self-driving cars, product quality classification, automatic print quality inspection, text recognition, texture recognition, tracking and positioning, and machine vision image recognition.
Finally, let's talk about how difficult machine vision is to learn. Honestly, machine vision isn't actually that difficult, but it does require a certain foundation. You need to understand what machine vision technology is, and have some knowledge of C or C++. You don't actually need a vast amount of knowledge. However, you will need patience and effort during the learning process, because there is a lot to learn. Whether you are a college student or a university student, as long as you work hard, you can quickly enter the field of machine vision.
Simply put, what you need is a basic understanding of C++ (not necessarily very advanced, but you should know a little), a basic understanding of digital image processing, knowing the function of each algorithm and understanding its general principles, and learning QT. That would be considered getting started. After that, it's all about continuous learning and accumulation on your own.
The machine vision industry has a very promising future. Keep it up!
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