Computer vision (CV) is a cutting-edge technology aiming to replicate the impressive capacities of human vision. It tries to extract meaningful information from images and videos, using computers, in an automated way.

Where can we apply computer vision?

There’s an ever-increasing range of applications of CV in every human domain:

• Retail -Tracking customers’ behaviour to increase sales and prevent shoplifting; automated out of stock notifications;

• Production – Identifying item defects or potential machine breakage;

• Healthcare – Making a more accurate diagnosis, helping visually impaired people navigate indoors;

• Insurance – Estimating costs, analysing claims, and sending them to appropriate agents;

• Agriculture – Disease prediction, spraying herbicides precisely;

• Security – Analysing images from surveillance cameras, facial and fingerprint recognition.

Mentioned applications constitute only a small part of the whole spectrum. So, everyone recognises the possible benefits and wide ranging scope of using computer vision services in any professional sector to improve productivity, reduce costs and increase client experience. Let’s get a look at some of the most common CV types used today.

Image classification

Image classification is the simplest CV type with the main aim to classify the image into one or multiple categories. Instead of providing a precise computed description of any item class, they show the computer numerous images of class representatives. The computer studies the images and includes an item into a particular class.

Localisation

Localisation presupposes defining a bounding box that encloses the object in the image. This CV type allows for the automatic cropping of objects in a set of images to build a dataset of images quickly.

Object detection

Combining location and classification, object detection classifies many objects instead of one. In this way, it presents information about the class and location of an object shown with a bounding box delineating it. Object detection is applied to counting people at public events or the number of objects in stock. 

Object tracking

Object tracking technique tracks an object that is in motion over time. This CV type is critical to autonomous vehicles and human tracking systems in the stores, at security checks, or sports games. 

Semantic segmentation

Semantic segmentation divides the whole image into pixel groups, labels and classifies them. Instead of a rough bounding box, it shows the pixel-level location of objects in the image assigning every pixel to some class.

Instance segmentation

Similar to semantic segmentation, instance segmentation classifies an object and identifies its boundaries at a pixel level. But it also discriminates between different instances of one class (e.g. colour difference) and how they relate to one another.

We also have to point out some other CV types worth mentioning:

• Key point detection (detects key points (of a face or body) to reveal more detail and track an object);
• Depth perception (estimates the 3D depth of the objects);
• Image captioning (creates a caption describing an image).

Apart from the mentioned types of CV, there are many others used in computing systems, each of which is conducive to business advancement.

Conclusion

The market growth rate of CV increases rapidly with multiple companies worldwide applying different CV systems to enhance their efficiency and effectiveness. A successful CV project results in plentiful benefits for a business including:

• Reduced costs;
• Increased revenue;
• Operational benefits (increase in production speed and overall labour productivity);
• Quality improvement (defect identification and increased accuracy);
• Data collection and parameter tracking;
• Increased security.

We have considered major types of CV and highlighted with endless applications in what way businesses and their clients can benefit from CV implementation. This proves that leveraging CV techniques for businesses has a definite impact on performance enhancement and business growth.