Data – we usually think about numbers or personal, sensitive information when we hear about data analysis, but what about images? Shapes? Could they be important for business? Can analysing images be a way to improve your business? Certainly! Many innovative companies are benefiting from applying image segmentation technology in their processes.
It is more and more common to entrust tasks to machines if they can be performed better and faster this way. Image segmentation is used in many modern companies for processes automation in many areas. There are many techniques of image segmentation. Learn what the differences are and how you can use this technology to improve the efficiency of your business processes.
Image segmentation – what is it?
Data is more than just words and numbers. Information on films or images can be very useful for business. Machines cannot understand what a photo or film presents until this resource is analysed. There are ways to make this “analysis” simpler and faster – this can be done with many image segmentation algorithms.
How do you find those valuable elements? An image is a collection of various pixels. During image segmentation, similar pixels are grouped together (those groups are called Image Objects). Then pixel-wise masks are created for the objects on the image so you can learn more about elements’ shapes. In all images we can find regions which do not contain important information, which is why dividing them into many parts (segments) is interesting and useful. You don’t waste time processing the whole image, but can simply focus on some specific elements – that is, in short, how image segmentation works.
What kind of techniques can be used in image segmentation?
Collecting similar pixels in the image can be done in two ways – by similarity detection (also called the Region Approach) and discontinuity detection (also known as the Boundary Approach). The first approach uses special algorithms to search for similarities, while the second one, on the contrary – uses algorithms (such as Edge Detection, Point Detection, Line Detection) in order to find discontinuity. The various techniques of image segmentation are all examples of one of these two approaches.
In the Threshold Method, the pixels are divided based on their intensity. Pixels are compared to each other by their threshold values. This is a good technique if the image consists of objects which have more intensity than the background or other elements of the image which are not important for the analysis. You may have heard about Simple Thresholding and Otsu’s Binarization. Those are two kinds of this technique. In Simple Thresholding, pixels are replaced with black (when the intensity of the pixel is less than the threshold) or white (when the intensity is more than the threshold), and the threshold is constant. The Second type of this method is usually used for document scans, pattern recognition etc. It makes it possible to convert grey images into binarized images in order to process them more efficiently. There is also a third type of this method, which is Adaptive Thresholding. It is a good approach when we need to be able to change the threshold for different components of the image.
Edge Based Segmentation
The process of locating edges in an image is called Edge Based Segmentation. It helps understand image features during analysis, as edges are believed to contain important information. This process filters out less relevant data, only keeping the information necessary to solve the given business problem. Edges are detected by spotting numerous discontinuities in grey level, colour, brightness, saturation and other factors. There are two types of edge detection algorithms – gradient based and gray histograms. It is used in areas such as image processing and computer/machine vision.
Region Based Segmentation
A method that involves creating segments by dividing the image into components with similar characteristics is known as Region Based Segmentation. Some small parts of the input image are chosen and then other pixels are added or removed to create groups. Region Growing is a type of this segmentation technique. It starts from a small set of pixels and accumulates more by comparing other pixels based on particular pre-determined similarity constraints. If they are similar enough, they’re added and the region grows bigger till the moment it can go no further with this process. There are also segmentation techniques called region splitting – which is dividing an entire image into regions with similar characteristics – and region merging – joining adjacent regions which are alike.
Other types of image segmentation
There are many techniques of image segmentation and it would take a lot of time to list them all, and even more to describe them properly.There are methods that use algorithms that don’t use any pre-defined sets of features or classes (Clustering Based Segmentation). Some of them are quite popular, as they’re both simple and efficient, which makes them great for solving various business problems. Many advanced methods like Artificial Neural Network Based Segmentation methods have also been introduced. This one uses neural networks for Image Recognition. Artificial intelligence enables automatic processing and identifying the components of images (elements such as faces or text – even hand-written).
Nowadays with the recent advancements in the Deep Learning area the neural network-based approaches are prevailing. Despite the fact that they are more compute intensive that standard approaches like region based segmentation they provide unrivaled results. The precision of region detection in images performed by neural nets is on par with precision of human annotators.
In recent years along with the popularization of special chips (GPUs and TPUs) that help parallelize neural net computations for tensor intensive operations, use of Deep Learning is within reach of every company.
Many cloud providers like AWS or GCP provide dedicate APIs and services that allow business to leverage power of AI tools for image segmentation and object detection without the need of provisioning sophisticated hardware. Cloud tools for image understanding operate on models that have already been pre trained on huge amounts of image data and are ready to be reapplied to any business process that needs automatic processing of images.
Can image segmentation be used for business automation?
Image segmentation has been an answer for many business problems, making it possible to automate many processes. Fast shape recognition is crucial for many industries – especially for hospitals and clinics, leading to better and immediate diagnosis, but the benefits don’t end there. Nowadays, it is used by many companies in agriculture, mining, geo-sensing, robotics, self-driving vehicles, security and many other fields. Image segmentation techniques, along with other technologies, can be used to make businesses smarter and more efficient.
Robotics and manufacturing
In today’s world we already use a lot of smart devices, and it is highly possible that there will be more and more of them in our houses and in our industries. Applications that provide operational guidance to devices need to process and analyse images. Machine vision systems use digital sensors in special cameras to capture images and send them to other systems that perform analysis using image segmentation methods. In business, this can be used in many ways – for example, for quality control in production.
Face and object recognition
this can be used for security – it is already used by applications and devices (personal computers and smartphones) to ensure safe access. Algorithms are used to verify facial features. This technology can also be used in cars to detect the presence of pedestrians and prevent accidents – by alerting drivers or braking automatically. Image segmentation enables self-driving cars to identify and locate other vehicles and objects on the road as well.
Image segmentation has certainly made the diagnostic process more efficient. Thanks to this technology, the search for cancer and other pathologies has never been easier. Particular types of tissues can be identified with clustering. This method not only allows for faster diagnosis, but also reduces the possibility of human error.
Algorithms for image segmentation can be also applied in automatic inventory analysis within shops. Analyzing the feeds from cameras around the shop algorithms can infer if any product on the shelf is out of stock and trigger an alert for the staff to engage. Moreover AI based models are also used to verify shelf share, enforce the planograms are followed and automatically cluster products in blocks.
Image segmentation is a powerful technology which can improve any industry’s efficiency. Contact us to learn more about how it can make your company more up to date.
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