The continuous advancement of neural networks and deep learning brought significant progress in computer vision. This is the area of artificial intelligence that allows computers to see and understand visual information as humans do. Thanks to computational techniques, machines can now analyze images and videos, extracting the valuable information for future use.

The popularity of machine vision usage continues to grow, conquering all industries, from manufacturing sector to retail, from media to healthcare. It saves us time, processing the visual information much quicker. Thanks to computer vision we enjoy such technologies as facial recognition on our smartphones, automated checkout in shops, and traffic detection in our GPS.

So, how exactly computer vision works, and how can it help us extract valuable information from the world around us? Let’s see!

How Does Computer Vision Work?

Computer vision operates based on huge amounts of raw visual data. Two techniques are used for the training before the machine starts differentiating between the objects: deep learning and convolutional neural network (CNN). Deep learning, which is a type of machine learning, allows the computer to teach itself based on the set-up algorithms. Thanks to convolutional neural networks, the machine “sees” and detects what it sees.

Before the machine actually starts differentiating objects, it goes through the process of training, which consists of several stages. When the dataset of images is collected and identified, the very first step is to annotate them. Data annotation is important for almost all types of computer vision services, be it object detection, image categorization, or scene recognition.

Thanks to data annotation, the information from images or videos can be labeled, providing accurate data for further ML training. It’s the foundation if we want to get effective final model performance. Annotated data also helps the models to generalize information, prepare them for specific projects, and decrease the biases.

Depending on the task, data annotation is used for such processing of image or video data: image segmentation, object detection, image classification, semantic segmentation, and object recognition, among many others.

Once the collected data is annotated, the computer vision training goes through the following steps:

• Data augmentation. To suit the needs, the existing images can be resized or adjusted to improve its accuracy.
• Validation and testing. During and after the training, we usually test the model’s performance to ensure that the final result generates new needed data.
• Optimization. Based on testing and validation results, we adjust the computer vision model and can add changes to its architecture.
• Deployment. Once the model is trained and all the results are successful, we can deploy the model into our application. Based on trained data, the model can now predict the presence of labeled subjects or objects.

The Most Popular Computer Vision Tasks in AI

Computer vision continues to evolve, advancing the algorithms and beating a path through new industries. However, there is a stable set of tasks that computer vision can perform today:

• Object detection. Location and identification of objects within image or video based on defined parameters. It involves detecting objects, people, vehicles, or specific items.
• Image classification. Labeling of the image or a video as a whole and within a class. Within the context, the image classification can identify whether the image contains a fruit, an animal, or belongs to a certain class.
• Facial recognition. Based on an individual’s unique facial features, the technique verifies and tags an individual.
• Object tracking. Usually used in a series of images or real-time videos, this task follows the object once it is detected. It can track the object over the whole time of the video and understand its movement.
• Semantic segmentation. It divides the image into different segments, assigning labels to spatial layouts. It allows machines to differentiate between the objects.

The implementation of one or more CV tasks increases the efficiency of applications, providing extensive insights from visual data. They also open new horizons for automation and more accurate decision-making.

Computer Vision & Its Real-Life Applications

More and more applications that appear today in the real world show the potential of computer vision across industries. Massive amounts of visual data coming from security and traffic cameras, smartphones, and other visually instrumented devices is a major factor fueling the expansion of these applications.

Some industries are a couple of steps ahead, investing in AI and benefitting from endless opportunities the computer vision brings today. Here are some of the real-life examples:

• Inventory management. Computer vision helps with monitoring stock levels and detecting misplaced objects. This is especially popular in retail. An exciting example shows Walmart, who trained the AI algorithms in a way to keep their products always in stock. Based on what is visually detected, an automatic alert signals when the product availability approaches its defined minimum.
• Remote monitoring. It’s not only about security surveillance, but also monitoring the patient conditions in healthcare. Computer vision is also widely used for diagnoses, x-ray imaging, or medical scans.
• Quality control. Computer vision greatly assists in production and manufacturing. It helps to detect defects and meet the quality requirements.
• Client authentication. Previously mentioned, facial recognition has been largely used by such giants as Apple and Google for unlocking our phones. It is also popular for all kinds of security control and access.
• Traffic management. With computer vision, more and more cities become smarter, showing traffic in real time.
• Environmental monitoring. Natural monitoring has become more effective. Computer vision contributes to wildlife preservation and monitoring of wildlife populations. It also assists us in assessing the levels of pollution and the general conditions of air.

Let’s Wrap it Up

The transformative power of computer vision in extracting valuable insights from images and videos has brought new opportunities. Across industries, from retail to healthcare, from agriculture to manufacturing, computer vision transforms the way we interact with visual data.

With sophisticated algorithms and annotated datasets, computer vision goes beyond human limitations. Tasks become more effective with help of image classification and object detection, with facial recognition and semantic segmentation. As we are now able to identify anomalies and derive meaningful information from visual content, we can improve our decision-making and progress with automation.