Image processing applications in data science – practical know-how

Image processing is one of the fastest-growing technologies that is successfully used in numerous business sectors. Why is this solution so popular? In this article, we present the theory as well as practical examples of possible applications and explain why more and more companies decide to invest in this technology. Enjoy your reading!

What is image processing?

Image processing is one of the fields of artificial intelligence, which involves converting images into digital form to allow computers to process them through an algorithm.

This way, machines can identify and extract information from objects in a specific image or video file. However, to correctly recognise objects in pictures, they need a vast database and a model to replicate the way humans perceive this world.

Digital image processing uses machine learning models such as deep neural networks to allow more various actions, such as adding filters to an image or enhancing some aspects to improve its quality.  

Image processing — how does it work?

Image processing typically consists of three key steps:

1. Image capture — drawing an image from a specific device (such as a camera) for further analysis;
2. Image interpretation —  the analysis of the image by the system, which detects and compares specific patterns in the picture;
3. Acting on the image — depending on the user's needs, the user can perform specific actions on the image, such as obtaining detailed information about the elements in the image.

Possibilities of image processing

Image processing is the starting point to perform a wide variety of tasks, such as:

• Object classification – allows you to identify what is contained in an image;
• Object localisation – determines the location of a single object in an image;
• Object detection – specifies the location of multiple objects in an image;

• Object tracking – identifies objects in the video;

• Optical character recognition – allows a computer to read physical documents, such as scanned papers;

• Semantic segmentation – focuses on grouping pixels of an image belonging to the same object class and giving them an appropriate label;

• Modulated detection – a new method that combines image classification with text classification techniques to help find an element in an image based on the text that describes it.

Image processing — use cases in particular industries

The use of imaging technology works well in various industries and depends on the needs of a particular business. However, there are a few industries for which these types of solutions can be beneficial:

Agriculture

With digital image processing, farmers can both reduce the cost of crops and make them more eco-friendly. For example, by analysing and monitoring watering, they can manage water usage more effectively and water only the crops that need it at a given time.

Another area to apply this technology would be monitoring fields before collecting the crops to decide where to harvest. In addition, advanced image processing technology can detect weeds and thus improve plant health.

Another exciting solution is a technology that uses hyperspectral imaging, which collects and processes information from the electromagnetic spectrum. Using satellites to observe the reflection of waves invisible to the human eye can detect even the tiniest changes in the physiology of plants and then correlate them with the spectrum of reflected light. With this technology, it is possible to increase the efficiency of irrigation of large agricultural areas, resulting in the highest potential yields.

Manufacturing 

In the manufacturing industry, image processing is mainly based on cameras placed within the production line. It allows for real-time monitoring of products during the production process.

What is essential, such cameras can also be installed in places inaccessible to people, where toxic fumes may be present. It is worth noting that this method is much cheaper to implement and maintain than laser scanning, which is also popular in this field.

Accounting

Like other AI-based solutions, image processing saves a lot of time. In the case of accounting, it can be sorting and searching for thousands of documents available in a paper version. Accounting firms can quickly upload printed files from clients to their database and manage them easily by using optical character recognition.

Insurance

Nowadays, natural disasters are becoming more and more common, causing numerous losses and, consequently, an increase in insurance claims. Traditional methods of damage documentation are slow, expensive, and sometimes even risky.

The solution is to use modern technology, including drones that use image processing for automated damage detection and analysis. The PwC report "Clarity from above" stated that the application of such solutions in the insurance industry could save $6.8 billion annually. 

Security

Thanks to facial recognition technology, security systems can recognise and remember household members and then take appropriate action (such as calling a security company) when intruders are detected. 

Moreover, facial recognition is also used by services such as security and the police. For example, the British police use this technology to scan the public during major events. The AI-based system helps officers detect suspicious people. If it finds someone at least 59% similar to the wanted criminals, a match is sent to the officer to double-check. According to UK authorities, the implementation of this system has increased the efficiency of policing.

Photo editing & social media

In the Internet era, some of the most popular uses of image processing can be found in photo editing applications and social media (including Instagram, Facebook, Snapchat), which offer features such as:

• adding filters,
• removing or changing the background, 
• improving photo quality, 
• adjusting contrast,
• retouching,
• and much more.

With the ability to detect objects and people in an image, these applications can determine where things are in real-time and adjust filters and dedicated overlays accordingly.

Healthcare

Did you know that as much as 90% of data in healthcare are medical images? Talking about the use of AI in the medical industry, it is worth noting that many innovative solutions are based on image processing. AI technology in the analysis of medical images can significantly reduce the time patients have to wait for diagnosis, increase the efficiency of doctors and entire medical facilities, and most importantly, improve diagnostic accuracy and help many more people.

E-commerce

Displaying similar products to customers in online stores is a great solution to reduce the risk of shopping cart abandonment. How does it work? The software, using image processing, analyses what type of products the users view on the website and then displays similar or complementary products to them. It has been used for years by the biggest global giants such as Amazon. Apart from increasing the probability of purchase, it is also a great benefit for customers who can easily search for similar items and find the best product for themselves.

An exciting and innovative application of image processing in e-commerce is the display of contextual ads, i.e., ads that are contextually linked to the website's content. What is essential is that they are entirely privacy-compliant as they do not depend on cookies. As privacy regulations become more stringent, these ads may become more popular soon.

Top 6 business benefits of image processing 

1. Increased customer satisfaction

Thanks to solutions such as displaying similar products in online stores, customers will be more likely to visit your website.

2. Saving time and money

Image processing technologies can significantly reduce the time spent on specific activities, such as editing images or ​​damage detection.

3. Increased employee satisfaction

With automated image processing systems, your employees will no longer waste time on tedious and monotonous tasks such as manually categorising files or searching for documents. They will be able to take care of other, more exciting duties instead.

4. Reduced risk of "human error"

Machines are never tired to work and maintain the highest efficiency at all times. An exciting example is a model analysing X-rays created by an international team, including researchers from Google Health and Imperial College London. The results showed that the AI model was as good as the current system of two doctors reading the results twice. What's more, there was a 1.2% reduction in false positives compared to a single radiologist.

5. Improving safety

Image processing is becoming increasingly popular in producing cars enhanced with sensors to improve road safety. Moreover, this technology is helpful in anti-theft systems or at airports to detect potential threats.

6. Increase the effectiveness of advertising campaigns

Image processing technology allows marketers to target ads better and predict users’ behaviour. Analysing images makes it possible to learn about their interests and behaviours. It helps create more personalised ads that match their visual preferences.

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