11 August 2021

What is Image Processing?

Image processing is a set of computational techniques for analyzing, enhancing, compressing, and reconstructing an image to get an improved idea or extract useful information. It is a type of signal processing in which input is an image and output may be an image or characteristics/features associated with that image.

Nowadays, image processing is among rapidly growing technologies. It forms a core research area within engineering and computer science disciplines too.

What are those Image processing techniques?

• Geometric operation – Rotation, translation, scaling or wrapping to remove distortion.
• Segmentation – To divide an image into meaningful regions (e.g., background + foreground).
• Fast Fourier Transform -Used for noise reduction and to extract detail.

The three phases that comprise image processing are as follows:

1. Importing the image via image acquisition tools;
2. Analyzing and modifying the image;
3. Output in which result can be altered image or report that is based on image analysis.

There are two types of methods used for image processing, namely, analogue and digital image processing.

• For tangible copies like prints and photographs can utilize analogue image processing can utilize. When applying these visual tools, image analysts use a variety of interpretive basics.
• Digital image processing techniques allow for computer-assisted alteration of digital images.
• The three general phases that all sorts of data have to undergo while using digital techniques are pre-processing, enhancement, display, and information extraction.

What is the image recognizing process?

As mentioned, image recognition is the process of examining a large number of photos with a machine-learning algorithm to identify and categorize them. The procedure is carried out on a convolutional neural network, an artificial variant of a biological neural network in which mathematical functions substitute neurons.

To recognize images, a model must be trained through the input of vast amounts of visual data labelled to learn from it.

The model learns from the photos and labels, recognizing and categorizing many future visual data by referencing what it has learned.

How will the image recognition process be helpful for Insurance Industry?

The more data fed to a model, the more accurate it becomes at identifying and classifying images.

The reason insurers are deploying image recognition in the claims process is because it yields the most economic benefit.

Moreover, Insurance Industry experts believe that image recognition technology could be the next step in AI (Artificial intelligence) transforming the insurance process. Artificial intelligence is revolutionizing insurance, and new imaging technology, in particular, could change how insurers manage claims and risk assessment.

Such as, using drones and satellite imagery can help insurers understand the scope and scale of damage. For some time now, insurers have been using satellite imagery to understand their exposure. It’s much more accurate and quicker with image recognition, and predictive AI added to the process.

Insurers can be more prompt in following up with consumers after an occurrence to see if they can provide additional assistance, such as alternative accommodation.

Also, using landscape data to predict the impact of natural hazards like hurricanes, wildfires, hail, earthquakes, and floods.

We get an accurate risk score when we run this data through our predictive model, along with high fidelity data about property features.

What is Claim Assessment and uses of image processing

For insurers trying to implement AI into their operations, the claims process is a primary priority, but for many, image recognition technology is the missing link in achieving this goal.
Most commonly, claims automation refers to robotic process automation (RPA) software to perform basic data input tasks significantly faster than a human.

However, it’s growing more likely that end-to-end claims automation will become a reality soon.

Although, claims are a priority category for image recognition deployment because the data is readily available to insurers.

Industry experts expect image recognition technology will take hold in the motor insurance sector first.

Since today, most of the insurer’s goal is that everyone trying to reach the moment to understand whether they can explain from an image if a vehicle is repairable.

Currently, someone must take the vehicle to a garage, even though many of those automobiles are deemed unrepairable.

However, because of picture recognition, the sooner you can get that information, the faster you can decide whether to repair the vehicle or pay a claim.

Also, Insurers can use image recognition to understand features like what kind of roof a building has and estimating the square footage for a building, which has a benefit to underwriters.

Once an insurer has submitted their claim, the insurance company will assess it based on their story of events and supporting documents, such as medical records.

This assessment can take a while as the insurance company needs to establish that insurer’s claim is genuine.

Furthermore, nowadays, we can use a claim assessment system, computer software and other intellectual property used by insurance companies, but this is not limited to the software packages.

Final Conclusion

Last and least, Image Processing is a technical tool that we can use in the insurance process, especially in claim assessment. This tool makes smooth and productive in the insurance process.

Image Processing is a new trend, and which is expected soon in the insurance industry, therefore.

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Written by Siththy Waseema