AI is a general term that encompasses several specific techniques. In this article we will explore the machine vision (MV) and computer vision (CV). They are related to visual input, so understanding these advantages overlapping technology, limitations, and the best case scenario is very important.
Researchers in the early 1950s began developing computer vision techniques, from simple two-dimensional imaging start for statistical pattern recognition. Until 1978, when the Artificial Intelligence Laboratory at MIT researchers have developed a bottom-up approach, created from a 2D computer "sketch" inferred 3D model, the practical application of computer vision becomes apparent. Since then, the image recognition technique is divided into different categories by a general use case.
Computer vision and machine vision use image capture and analysis executor eye can not match the speed and accuracy of the task. With this in mind, to describe these closely related through their common technology may be more effective, they are distinguished by their specific use cases rather than their differences.
Computer vision and machine vision systems share most of the same components and requirements:
comprising an image sensor and lens imaging apparatus
can capture an image or frame grabber board (in some modern interface using a digital camera, no frame grab take control)
for lighting applications
software processing of an image by a computer system or internal, such as a number of "smart" camera
So what is the actual difference is? Computer vision refers to the automated image capture and processing, with emphasis on image analysis. In other words, the goal is not just computer vision to see, but also to processes and to provide useful results in accordance with observations. Machine vision is the use of computer vision in industrial environments, making it a subcategory of computer vision.
Computer Vision in Action
2019, computer vision is playing an increasingly large role in many industries. In digital marketing, the company began using image recognition technology to drive better business results and advertising. Due to the continuous improvement of the accuracy and efficiency of computer vision technology, marketers can now bypass the traditional demographic studies, and quickly and accurately sort out millions of images online. They can then carry out targeted marketing in the proper context, and it only takes a fraction of the time to achieve the same result.
Intelligent machine vision and factory
capacity to intuitively recognize poor product defects and process efficiency problems for manufacturers to limit costs and improve customer satisfaction is essential. Since the 1990s, a machine vision system has been installed in thousands of factories in the world, used to automate many of the basic functions of quality assurance and efficiency. With enhanced data sharing, high-precision drive systems using machine vision in manufacturing has begun to accelerate innovation provided by cloud technology. Manufacturers realize that the machine vision system is an important investment to achieve quality, cost and speed of the target.
Machine vision line of
the reasons detect defects and quickly mitigate these defects is an important aspect of any manufacturing process. Long Rui Chico (www.lrist) turned to machine vision solutions to proactively address the root causes and the occurrence of defects. To identify the complex variable definitions good products and bad products by the installation of cameras on the production line and training machine learning models in real time to identify deficiencies and determine where the defect occurs in the manufacturing process is so proactive steps can be taken.
Technical Note visual machine learning models
in order to achieve the target computer or machine vision, first need training to make "intelligent" machine learning model of your visual system. And in order to make an accurate model of machine learning, it requires a lot of annotation data, solution-specific 'reconstruction. Free public use data sets can be used to test the algorithm or perform simple tasks, but to make the most of the actual success of the project, the need for specialized data sets to ensure that they contain the correct metadata. For example, the implementation of the model requires a lot of computer vision image annotation to tag people, traffic signals, automobiles and other objects within the autonomous vehicle. Anything less than total accuracy will become a huge problem of autonomous vehicles.
Related technologies have different use cases
, although the boundaries between the computer vision and machine vision has been blurred, but both cases preferably be defined by its use. Computer vision for automated image processing Traditionally, machine vision is the actual application of computer vision interface, such as a factory production line.
Custom machine vision service
modern visual system is designed to provide improved image quality, is an image recovery, image coding and image interpretation are the ideal choice. Whenever industrial applications require identification, or when measuring guide, machine vision is a widely used choice.