Computers that make decisions on their own, based on the environment they physically
are in, are the new frontier of artificial intelligence evolution, called Machine Vision, which allows computers to see and analyze their surroundings. It is an algorithm that provides spatial awareness to the computer, enabling the machine to develop a visual perception in the same way humans do.



Machine vision has provided new forms of automation not previously imagined, such as real-time parts tracking, enhanced quality inspection, product defect detection and more efficient and accurate safety systems. This allowed the technology to gain notoriety and be disseminated among numerous fields of society.


How it works

The first steps in Machine Vision date back to the 1950s and 1960s, with research on pattern recognition and image processing. It is known that there is no single developer of the Machine Vision concept, it is a multidisciplinary field that has evolved over time with contributions from several researchers and institutions around the world.

However, there are certain fundamental features that makes Machine Vision to be what it is, which is the case of Hough Transform, an algorithm proposed by Paul Hough in 1962 used for detection of lines and geometric shapes in an image, together with Machine Learning based algorithms, forming the main elements of Computer Vision data processing. Though, the main catalyst for its development is, without a doubt, R&D funding from Big Techs such as IBM, Microsoft, Google and Amazon.

In the knowledge of that, Machine Vision functions through high-definition cameras that capture images from the surrounding environment. These images are then trained as information through visual refinement algorithms, where filters are used to enable the recognition of behavioral patterns in the images.



It uses a convolutional neural network (CNN), a class of neural network that uses convolutional layers to filter useful information from images, to analyze and transform them into numerical representations. Then, the filters of the convolutional layers are automatically adjusted to extract the most relevant information for a specific task, establishing labels based on patterns. For example, in object recognition, CNN filters out information about the shape of objects, while in other kinds of elements, like birds for instance, it focuses more on color differences, as different types of birds have more distinct color differences than shape differences.

Finally, through the essence of any artificial intelligence, the system learns from its occasional mistakes and experience, constantly adjusting and improving its behavior.

However, cameras alone won’t do the job; appropriate lighting is required, as well as the implementation of specific lenses and sensors. An adequate processor is needed to support an I/O for communicating the information acquired from the captured images. This allows the computer to analyze information with a much broader sensitivity to magnetic waves than any human being, reaching levels of infrared, ultraviolet, and X-rays.


Will society benefit from Machine Vision?

The main frontier of Machine Vision lies in the industry, where its efficiency is already
recognized in process control, enhancing automated quality inspection, defect detection,
component and packaging tracking, corrosion maintenance, among others. It is noteworthy that the production of scientific knowledge has already advanced significantly in this area, shown by the massive amount of studies on Industry 4.0.

In addition to industrial purposes, Machine Vision is gaining ground across various
sectors. In agriculture, its use enables the improvement of crop monitoring processes, detection of diseases and pests, and optimization of harvests. It is an incredibly powerful tool for facilitating a range of activities for any company and for society as a whole.



Machine Vision serves as a highly accurate pattern recognition tool, enhancing facial recognition, signature analysis, video surveillance, and detection of suspicious activities. it also extends to areas such as medicine, aiding in medical diagnosis, analysis of medical images, patient monitoring, and identification of cancer cells.

And it won’t be long before this technology reaches the hands of the common citizen,
where it will be integrated into photography apps and social media, used to recognize faces and exact locations around the globe, identify people in photos, and apply automatic filters.

The areas where Machine Vision is applied may vary according to what the system is programmed to do, such as:



The algorithm supports producers in detecting problems in the crop that can bring down the productive potential, such as diseases, pests, weeds or if the area is compacted or has low soil nutrition. Automatic detection enables constant monitoring and searching for crop anomalies, based on images captured by field surveys or obtained by cameras attached to drones or tractors.




Computer Vision models have exhibited an impressive level of accuracy in tasks related to diagnosis, especially when it comes to brain tumors. Early detection of brain tumors is crucial as they have the potential to spread rapidly to other areas of the brain and spinal cord if left untreated. By leveraging computer vision technology, medical professionals can streamline the detection process, reducing the time and effort required for diagnosis and potentially saving patients’ lives.


Oil and gas industry

Computer Vision algorithms are used in this sector for detecting corrosion and other anomalies in production assets; it encompasses the use of drones and 360 degrees cameras for reality capture, sending those images to the AI to recognize patterns of risk within the company’s production installations. The use of this technology reduces costs, downtime and the number of people on board exposed to inherent risks.




In addition to data engineering, integrating Computer Vision technology to digital platforms contextualizes every sort of data that the operator of an oil and gas installation needs to properly engage with that specific part of the asset, bringing to the table a much more integrated and flexible system with faster solutions not only to corrosion detection, but to the whole integrity management of the asset.


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What does the future say about Machine Vision?

Machine Vision is creating business opportunities and entrepreneurship, enabling the development of innovative products and services based on computer vision. The ability to understand and transcribe images and videos will create new possibilities to enhance sectors such as manufacturing, medicine, security, transportation, agriculture, and many others. And with the continuous advancement of technology, it is likely that computer vision will play an even more significant role in several areas of our lives, making them more convenient, secure, and efficient.

But, what makes Computer Vision having immense potential as a tool isn’t its ability to the reality capture or face recognition, but its ability to keep learning from its previous experiences, allowing its constant enhancement. This way, decision making professionals can prioritize what they find more useful in their AI diagnosis, avoiding wastes of time or other resources and increasing accuracy and efficiency.



Machine Vision represents a significant advancement in artificial intelligence, enabling computers to see, analyze, and understand their surroundings in the same way the human visual perception does. This technology has already made a significant impact in various industries, such as oil and gas productions, agriculture, medicine, and transportation, by enhancing automation, improving quality control, and enabling new applications through pattern recognition and deep learning algorithms.

Furthermore, it won’t be long until this technology is integrated into all processes of society, reaching a scenario in which AI technology facilitates the decision making of every citizen.

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