Within artificial intelligence, it is one of the fields that has developed the most in recent years artificial vision, the discipline that allows computers and other systems to extract information from digital images, videos, and other “visual” information sources in order to use that data to perform tasks such as object recognition, motion tracking, face detection, and more.

In fact, its applications are so diverse, from pattern recognition and image classification to vision in autonomous vehicles or inspection and quality control in industry.

According to the latest study published by Kings Research, the artificial intelligence market in computer vision was valued at $17.4 billion in 2022, but is expected to reach more than $206 billion by 2030.

How does computer vision work?

As with most artificial intelligence systems, training computer vision algorithms requires massive data intake. For example, to teach a computer to recognize the exhaust of a car, it is necessary to “feed” it a large number of images of exhausts in different forms so that it learns the differences and can recognize them. Once the model is trained, the algorithm typically goes through the following phases:

1. To take a picture or video: The process begins with taking pictures or videos using cameras or other capture devices.
2. Preprocessed: Before analyzing images or videos, it is common to perform a number of pre-processing steps to improve the quality of the data. This may include removing noise, adjusting lighting, or correcting distortion.
3. Feature Extraction: At this stage, relevant features are identified in the images. This can include edge detection, object segmentation, color identification, texture extraction, etc. These characteristics help define what will be analyzed in the image.
4. Data representation: Once the features are extracted, they are converted into data that a computer can understand. This can include converting images to numeric fields or vectors.
5. Analysis: This is where artificial intelligence algorithms like neural networks and algorithms come into play. deep learningwhich were previously trained to recognize patterns and perform specific tasks such as object detection.
6. Decision making: After the calculation phase, computer vision makes decisions based on the processed information. This may include, for example, the detection of anomalies in this object.
7. Interaction or Action: Depending on the application, the results can also be used to interact with the system, which could include controlling robots, triggering security alarms, guiding autonomous vehicles, or generating messages based on visual data.

The role of CCN

In the analysis phase, almost all the main roles are taken over by a network known as CNN (Convolutional Neural Network), which is a type of artificial neural network designed specifically for visual data processing.

As we have already mentioned, this type of neural network is able to identify important characteristics in images such as edges, textures, patterns and objects. For this they use convolution layers apply filters and detect specific elements in certain areas of the image.

CNNs learn hierarchically, meaning that as new layers are applied, they can detect increasingly complex features. For example, early layers can detect edges and colors, while later layers can recognize more complex shapes like faces or objects, repeating the process over and over again.

After extracting features from images, these same networks are commonly used for tasks such as image classification (e.g., determining whether an image contains a cat or a dog) and object detection (e.g., locating and labeling multiple objects in an image). image).

This whole process, for example, allows Google Translate users to simply point their phone’s camera at a sign written in another language and get it translated into their preferred language almost instantly; or drivers of AI-enabled vehicles who have since had to worry a little less about the possibility of running a red light The signal is recognized by the car itself.

The possibilities of computer vision are almost endless and can be directly applied to virtually any sector and industry. But putting them into operation also means having the appropriate hardware and software to ensure maximum performance for the company at all times. We talk about all this in ‘Artificial Intelligence: Real Value for Your Company’, a comprehensive guide exploring how AI is affecting companies and how it will evolve in the coming years. Don’t miss it!