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Computer Vision

What is Computer Vision?

Computer vision is a field of AI that trains computers to interpret and understand the visual world. Using digital images from cameras and videos and deep learning models, machines can accurately identify and classify objects and then react to what they “see.”

How does Computer Vision work?

To make the machine recognize visual objects, you must train the machine with hundreds of thousands of examples. For example, you want someone to be able to distinguish between cars and bicycles. How would you describe this task to a human?

Typically, you would say that a bicycle has two wheels, and a machine has four. Or that it has pedals, and the machine doesn’t. In machine learning, this is called feature engineering.

However, as you might already notice, this method is far from perfect. For example, some bicycles have three or four wheels, and some cars have only two. Also, motorcycles and mopeds exist that can be mistaken for bikes. How will the algorithm classify those?

When you build more complicated systems (for example, facial recognition software), misclassification cases become more frequent. For example, simply stating the eye or hair color of every person won’t do: the ML engineer would have to conduct hundreds of measurements like the space between the eyes, the distance between the eye and the corners of the mouth, etc., to be able to describe a person’s face.

Moreover, the accuracy of such a model would leave much to be desired: change the lighting, face expression, or angle, and you have to start the measurements all over again.

What’s the difference between Computer Vision and Machine Learning?

Computer vision, unlike Machine Learning is a relatively new technology. While the researchers started working on developing computer vision technology back in the 1950s, it wasn’t until a few years back that the technology was matured enough to be used in scientific and everyday use. The earliest application of computer vision was simple two-dimensional imaging, performed by scientists to recognize statistical patterns. 

In 1978, the MIT AI Lab was able to develop a bottom-up approach for the use of computer vision, which allows the technology to be used in practical everyday applications. Ever since image recognition technologies have continuously evolved, they are segregated into various categories by use case.

Machine Learning, on the other hand, is about creating intelligent machines that can observe, analyze and learn from datasets. The concept behind the technology is the use of statistical learning and optimization methods to enable devices to observe, process, and identify patterns within a dataset. Like computer vision, it’s a sub-branch of artificial intelligence and is used widely across various industries and applications, including visual interpretation. Machine learning technology relies on data mining to identify complex data patterns and learn them for future models. Today, machine learning technology is widely used for image recognition. Various supervised and unsupervised models used the technology to analyze images and identify elements of interest from within the image.

Where can Computer Vision be used?

1. Computer Vision for Defect detection

The detection of defects is carried out by trained people in selected batches, and total production control is challenging. Human error happens, mistakes happen, but with computer vision, we can detect defects such as cracks in metals, paint defects, and bad prints in sizes smaller than 0.05mm. These vision cameras need an algorithm called the “intelligent brain,” which can differentiate what a defect is and what is not. This algorithm is designed and trained specifically for each particular application through images with and without defects.

 

2. Computer Vision for Metrology

With complex laser metrology equipment or probes, what has been done so far can now be measured using computer vision. The key to making a reasonable adjustment of the reference to measure with the necessary precision is to use the appropriate lighting for each type of material and work environment. Using artificial vision systems, we can measure variable part sizes, straightness, parallelism, and more.

 

3. Computer Vision for Intruder Detection

It is possible to differentiate between a fruit and a stone through hyperspectral cameras, which allows, especially in food, safer products for the consumer. Hyperspectral cameras can determine the type of material through the measurement they make of the wavelength. In this way, it is possible to distinguish a stone from a fruit, a plastic from a metal, or other combinations while the material is different.

 

4. Computer Vision for Assembly verification

Every day more and more complex assemblies are made, with more parts or connections. Computer vision allows us to verify, step by step, that each piece is in its place, or at the end of the process, that the final assembly is correct. This application is beneficial for assembling machinery, equipment, electronic boards, or pre-assemblies with a lot of complexity. These systems significantly reduce cycle times of very complex operations and reoperation times.

 

5. Computer Vision for Screen reader

Sometimes it is not possible to extract data from a display screen either because it is a closed supplier system or because that system is incompatible with the one installed. A solution to this problem is to install a computer vision camera to read the screen and extract the data that appears on it (temperatures, codes, tensions, etc.) any helpful information that appears on the screen and you need it). To do this, we look for the interest regions in which the information is located, we use a character recognition algorithm (OCR) to extract it, and everything perfect!

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Computer Vision

October 14, 2020

Table of contents

Key takeawaysCollaboration platforms are essential to the new way of workingEmployees prefer engati over emailEmployees play a growing part in software purchasing decisionsThe future of work is collaborativeMethodology

What is Computer Vision?

Computer vision is a field of AI that trains computers to interpret and understand the visual world. Using digital images from cameras and videos and deep learning models, machines can accurately identify and classify objects and then react to what they “see.”

How does Computer Vision work?

To make the machine recognize visual objects, you must train the machine with hundreds of thousands of examples. For example, you want someone to be able to distinguish between cars and bicycles. How would you describe this task to a human?

Typically, you would say that a bicycle has two wheels, and a machine has four. Or that it has pedals, and the machine doesn’t. In machine learning, this is called feature engineering.

However, as you might already notice, this method is far from perfect. For example, some bicycles have three or four wheels, and some cars have only two. Also, motorcycles and mopeds exist that can be mistaken for bikes. How will the algorithm classify those?

When you build more complicated systems (for example, facial recognition software), misclassification cases become more frequent. For example, simply stating the eye or hair color of every person won’t do: the ML engineer would have to conduct hundreds of measurements like the space between the eyes, the distance between the eye and the corners of the mouth, etc., to be able to describe a person’s face.

Moreover, the accuracy of such a model would leave much to be desired: change the lighting, face expression, or angle, and you have to start the measurements all over again.

What’s the difference between Computer Vision and Machine Learning?

Computer vision, unlike Machine Learning is a relatively new technology. While the researchers started working on developing computer vision technology back in the 1950s, it wasn’t until a few years back that the technology was matured enough to be used in scientific and everyday use. The earliest application of computer vision was simple two-dimensional imaging, performed by scientists to recognize statistical patterns. 

In 1978, the MIT AI Lab was able to develop a bottom-up approach for the use of computer vision, which allows the technology to be used in practical everyday applications. Ever since image recognition technologies have continuously evolved, they are segregated into various categories by use case.

Machine Learning, on the other hand, is about creating intelligent machines that can observe, analyze and learn from datasets. The concept behind the technology is the use of statistical learning and optimization methods to enable devices to observe, process, and identify patterns within a dataset. Like computer vision, it’s a sub-branch of artificial intelligence and is used widely across various industries and applications, including visual interpretation. Machine learning technology relies on data mining to identify complex data patterns and learn them for future models. Today, machine learning technology is widely used for image recognition. Various supervised and unsupervised models used the technology to analyze images and identify elements of interest from within the image.

Where can Computer Vision be used?

1. Computer Vision for Defect detection

The detection of defects is carried out by trained people in selected batches, and total production control is challenging. Human error happens, mistakes happen, but with computer vision, we can detect defects such as cracks in metals, paint defects, and bad prints in sizes smaller than 0.05mm. These vision cameras need an algorithm called the “intelligent brain,” which can differentiate what a defect is and what is not. This algorithm is designed and trained specifically for each particular application through images with and without defects.

 

2. Computer Vision for Metrology

With complex laser metrology equipment or probes, what has been done so far can now be measured using computer vision. The key to making a reasonable adjustment of the reference to measure with the necessary precision is to use the appropriate lighting for each type of material and work environment. Using artificial vision systems, we can measure variable part sizes, straightness, parallelism, and more.

 

3. Computer Vision for Intruder Detection

It is possible to differentiate between a fruit and a stone through hyperspectral cameras, which allows, especially in food, safer products for the consumer. Hyperspectral cameras can determine the type of material through the measurement they make of the wavelength. In this way, it is possible to distinguish a stone from a fruit, a plastic from a metal, or other combinations while the material is different.

 

4. Computer Vision for Assembly verification

Every day more and more complex assemblies are made, with more parts or connections. Computer vision allows us to verify, step by step, that each piece is in its place, or at the end of the process, that the final assembly is correct. This application is beneficial for assembling machinery, equipment, electronic boards, or pre-assemblies with a lot of complexity. These systems significantly reduce cycle times of very complex operations and reoperation times.

 

5. Computer Vision for Screen reader

Sometimes it is not possible to extract data from a display screen either because it is a closed supplier system or because that system is incompatible with the one installed. A solution to this problem is to install a computer vision camera to read the screen and extract the data that appears on it (temperatures, codes, tensions, etc.) any helpful information that appears on the screen and you need it). To do this, we look for the interest regions in which the information is located, we use a character recognition algorithm (OCR) to extract it, and everything perfect!

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