Computer Vision and Pattern Recognition – Examples of Real-life Applications
What is computer vision?
Computer vision (CV) is a technology that enables computers to get a deep understanding of situations presented in photos and videos. It enables the automation of tasks that only the human visual system was able to perform.
In order to understand digital images, first, computer vision services and solutions need to perform multiple tasks such as acquiring, processing, and analyzing data. All of these activities are possible thanks to neural networks that enable an iterative learning process.
Neural networks use pattern recognition in order to differentiate between various pieces of an image. Human help is not necessary as CV learns by itself from a huge base of uploaded images.
How does pattern recognition work?
Pattern recognition is a technology that focuses on distinguishing different data and later, segmenting it according to specific criteria. These processes are performed by advanced algorithms that are considered elements of machine learning technology.
The main goal of pattern recognition is to automatically determine regularities in data without human assistance. Types of data that are most often used in pattern recognition are texts, images, and sounds.
Pattern recognition models can be classified into three main categories:
- Statistical model – requires supervised machine learning. This model identifies where a particular piece belongs.
- Structural model – requires semi-supervised machine learning. It determines a complex relationship between different elements.
- Template matching model – most commonly used in plagiarism checking. This model matches the features of a particular object with predefined templates.
Computer vision and pattern recognition applications
Stock market forecasting and audience research
One of the areas in which pattern recognition software is very effective is stock market forecasting. Accompanied by data analytics, pattern recognition enables comparative analysis of the stock exchanges and later coming up with accurate predictions of the most likely outcomes.
In audience research, pattern recognition is extremely useful in user segmentation. The software analyzes available user data and performs segmentation while taking into consideration particular features.
Optical Character Recognition
Optical character recognition (OCR) is a technique used to analyze and then convert images marked as alphanumeric text into machine-encoded text. Optical characters can be found, among others, in scans of photos and documents, text transcriptions, and many more.
How OCR work? The optical character recognition algorithm compares a given document with a library of patterns in order to determine text fragments. Then, acquired outcomes are checked by the assistance language corpus and as a result, recognition without human assistance takes place.
Image pattern recognition
The main purpose of this technology is to make the computer understand what is on the picture. Image pattern recognition instead of recognizing (as OCR), describes what is on the picture. It enables accurate searching and comparing a particular image with others.
Image pattern recognition is mainly used in:
- Visual search – works analogously to alphanumeric search queries but with images. Users can search through a dataset of images, select personal preferences, and get adequate outcomes. This technique is widely used by e-commerce marketplaces and search engines.
- Face detection – an algorithm is able to recognize the look of a particular person while using a specialized labeled sample set. This technology is used by law enforcement for example to find criminals on the run. Face detection is also popular in social network services like Instagram.
Computer vision in healthcare
Most often medical data is image-based. That is why computer vision technology turned out to be of great support for healthcare professionals.
CV enables new methods of medical diagnoses through deep analysis of scans, x-rays, mammography, and many more. By identifying problems earlier and more accurately than done by humans, patients have higher chances of full recovery. Diagnoses made by computer vision algorithms turned out to be helpful also during surgeries.
Computer vision in agriculture
Computer vision algorithms are able to analyze the quality of grains when it gets harvested. It can also find the best, most optimal route to get through the crops.
Another CV application is enabling programs to identify weeds, separating them from the crops in order to spray herbicides, as desired, directly on weeds. Research shows that this solution will reduce the needed amount of herbicides by 90%. It will contribute to the decrease in costs and at the same time to develop more ecological agriculture.
Computer vision in manufacturing
A great example of an effective computer vision application in manufacturing is predictive maintenance. Cameras using computer vision technology can monitor equipment 24/7, look for potential problems, and prevent them before breakdown occurs.
Manufacturers highly benefit from CV tools that help them do their job more effectively, intelligently, and most importantly safely.
During packaging processes, quality checks run by computer vision will instantly identify defective products and drive conclusions on a possible cause.
To sum up
Computer vision and pattern recognition have a lot to offer both businesses and their clients. These technologies are still developing but there are already numerous services that are proven to be highly effective and beneficial. Let’s see what new solutions the future will bring.