What computer vision algorithms do

7 minutes to get you to understand how computer vision works.

Machine vision. basic steps of the algorithm;

1. Image data decoding.

2. Image feature extraction.

3. Identify the target in the image.

Machine vision is a branch of artificial intelligence that is developing rapidly.

To put it simply, machine vision is the use of machines instead of human eyes to make measurements and judgments.

The machine vision system converts the acquired target into an image signal through a machine vision product (i.e., an image acquisition device, divided into CMOS and CCD), transmits it to a dedicated image processing system, obtains the morphological information of the photographed target, and converts it into a digital signal according to the pixel distribution, brightness, color and other information; The image system performs various calculations on these signals to extract the characteristics of the target, and then controls the action of the equipment in the field according to the results of the discrimination.

Now there are many companies doing visual inspection, both at home and abroad, and many of them are very good.

It can provide complete machine vision software solutions, and can also provide customers with algorithm-level customization, covering all industrial application fields, with a wide range of applications. The application of machine vision will become more and more because the level of computing is getting higher and higher, and more complex vision algorithms can be processed; In fact, many things, including the now popular GPS, were first done by foreign companies, and the procedures were outsourced by Chinese;

Personally, I think the application of opto-electromechanical is very mature, and there will be no new things.

Image Preprocessing Algorithms:

1.Image gradient algorithms Sobel, Scharr

2.Corner detection algorithm Harris

3.Edge detection algorithm canny

Straight line detection algorithm hough

Image Feature Processing Algorithms:

scale-invariant feature transformation).

Acceleration robust features).

gradient histogram).

histogram) local binary mode).

6.Brute-Force (brute-force feature matching) common algorithms for machine learning:

Nearest neighbors) support vector machines).

mean) cascading classifier.

Random Forest) 6decision tree logistic regression).

8.gmm (Gaussian Mixed Model).

Self-organizing mapping).

matching matrix) convolutional neural network).

The terms machine vision and computer vision are used interchangeably in many contexts, but they are different in some ways.

Computer vision is a broader concept that focuses on enabling computers to understand and analyze digital images or ** like humans. Computer vision is a series of theories and techniques, covering everything from image acquisition, processing, and analysis to high-level image understanding and reasoning. Computer vision has a wide range of application scenarios, including autonomous driving, medical image Keeming analysis, virtual reality, augmented reality, etc.

Machine vision, compared with computer vision, focuses more on industrial fields and automation applications. Machine vision typically involves real-time processing and analysis of images for automated inspection, measurement, identification, and other tasks to improve productivity and quality. Machine vision systems often include hardware devices such as image sensors, light sources, and lenses, as well as software modules for image processing, analysis, and control.

Typical machine vision application scenarios include product quality inspection, defect detection, parts identification, positioning, and tracking on industrial production lines.

Overall, computer vision is a broader concept that encompasses a variety of theories and techniques for image processing and analysis, while machine vision is the concrete implementation of computer vision in industrial fields and automation applications. While the two terms are used interchangeably in many contexts, there are still some differences in their scope and focus.

Although computer vision and machine vision are similar in many ways, there are some key differences between them.

Objectives: Computer vision focuses on the study of visual perception problems from the fields of mathematics, computer science, and artificial intelligence. The goal is to teach computers to understand and interpret digital images or ** to achieve functions similar to those of the human visual system.

Machine vision focuses more on the use of computer vision technology to solve problems in practical engineering applications, and is usually applied in industrial environments, such as automatic inspection, quality control, manufacturing process monitoring, etc.

Processing flow: Computer vision typically includes multiple steps such as image processing, feature extraction, pattern recognition, and image understanding, which can be implemented by different algorithms and models, such as deep learning models.

Machine vision includes image acquisition, preprocessing, feature extraction, recognition and positioning, and decision output. It pays more attention to the optimization of the entire process and the practical application effect.

Applications: Computer vision has a wide range of applications, including autonomous driving, medical diagnosis, security monitoring, augmented reality, computer animation, etc.

Machine vision is mainly used in industrial fields, such as quality inspection, workpiece positioning, assembly, packaging and other links in automated production lines.

In conclusion, computer vision and machine vision overlap in many ways, but they differ in focus and scope of application. Computer vision focuses more on theories and algorithms, while machine vision focuses more on practical applications and engineering problems.

Computer spring vision and machine vision are just different application scenarios, Ming Hao is like pulling trucks and buses, yes, the focus is different, one focuses on artificial intelligence branches, and the other focuses on industrial applications! To put it simply, computer vision focuses on deep learning and software, and machine vision focuses on special recognition and has relatively high requirements for hardware, but with the development of higher and higher requirements for intelligent recognition, these two directions will penetrate and integrate with each other after all, and the difference is only limited to different application fields.