How To Implement Image Segmentation: Step By Step Method

We all took plenty of selfies? Have you seen how our AI smartphones were able to pick up distinct features of our facial expressions? We are able to do so because our brain has the power to detect and distinguish each part. And with the advent of technologies such as Image processing and AI our digital devices can do it too. This is called Image Segmentation. In today’s article, we’ll explore image segmentation in digital image processing. You can master them with Digital Image And Video Processing Certification Courses. So let’s dive in.

Also Read: Free digital image and video processing courses

What is Image Segmentation?

It is a technique for breaking down a digital image into subgroups called Image segments. This reduces the image's complexity, making future processing or analysis easier. Labels are assigned to pixels during segmentation. It's the first step in picture processing. Computer vision processes would be extremely difficult without image segmentation in image processing.

You can separate and group certain pixels from an image using image segmentation algorithms, assign them labels, then classify additional pixels based on these labels. You can draw lines, set borders, and isolate specific objects (critical components) in a picture from others (unimportant components).

For supervised and unsupervised training in machine learning, you can use the labels you created via image segmentation in image processing. This would enable you to solve a wide range of business issues.

Also Read: Coursera Digital Image and Video Processing courses and certifications

Importance of Image Segmentation in Image Processing

It is important as it extracts the items of our interest for additional processing such as description or recognition, segmentation is a key stage of the image recognition system. The classification of picture pixels is accomplished using image segmentation.

Image segmentation in image processing is a big part of computer vision, and it's used in a lot of different businesses. The following are some of the important areas where picture segmentation is widely used:

Also Read: 18 Courses On Image Editing For Beginners

1. Face Recognition

Image segmentation is used to identify your face via the facial recognition technology in your iPhone and advanced security systems. It must be able to recognise your face's distinctive features in order to prevent unauthorized access to your phone or computer.

2. Number Plate Identification

To charge penalties and assist with searches, many cameras and traffic lights utilize number plate identification. A traffic system may recognise a car and obtain information about its owner using number plate identification technologies. It divides a number plate and its data from other objects in its vision via image segmentation. For governments, this technology has greatly streamlined the fining procedure.

Also Read: Free photography certification courses

3. Image-Based Search

Image Segmentation algorithms help Google and other image-based search engineers to recognize the features in your photo and connect their results with the right photos they discover in order to return search results.

4. Medical Imaging

We employ picture segmentation in the medical field to find and measure tissue volumes, run virtual surgery simulations, identify cancer cells, and conduct intra-surgery navigation. In the medical field, medical image segmentation has numerous applications. It aids in the identification of affected areas and the formulation of treatment plans.

Apart from medical image segmentation, there are also applications in other areas such as agriculture, security, and a variety of other fields. Image segmentation techniques are becoming increasingly popular as our computer vision technology progresses.

Some manufacturers, for example, have begun to use image segmentation techniques to identify defective products. Only the basic components from the object's image would be captured by the algorithm, which would then identify them as flawed or optimum. This technology lowers the danger of human error while also increasing the efficiency of the testing process. C, C++, Python, and Matlab are often used to categorize images.

Also Read: Free photography and video certification courses

Image Segmentation Techniques

Image segmentation is a vast subject with numerous approaches. The parameters that can be used to classify image segmentation in image processing:

1. Approach-Based Classification

Image segmentation is, at its most basic level, object recognition. Without first recognising an item, an algorithm cannot classify the various components. Object identification is used in all picture segmentation solutions, from simple to complex.

As a result, image segmentation techniques can be categorized according to how computers recognise things, which entails gathering similar pixels and separating them from different pixels. This task can be completed in one of two ways:

Also Read: 10 Paid Courses in Digital Photography Every Aspiring Photographer Needs To Check Today

2. Region-based Segmentation Approach (Detecting Similarity)

This method uses region growing, region merging, and region based segmentation to detect comparable pixels in a picture based on a threshold. This strategy is used in clustering and other machine learning methods to find unknown traits and properties. This is how classification algorithms discover features and separate image segments based on them.

Boundary-based Approach (Detecting Discontinuity)

The boundary-based method of object recognition is diametrically opposed to the region-based method. The boundary-based approach, unlike region based segmentation, discovers pixels that are unique from one another. Edge Detection, Point Detection, Line Detection, and other algorithms employ this method to detect and separate the edges of different pixels from the rest of the image.

Also Read: Which are the best certification courses for Photography in India

Technique-Based Classification

Both ways, image segmentation techniques differ. These tactics are used based on the type of image to be processed and analyzed, as well as the desired consequences.

We may categorize picture segmentation methods based on these parameters:

Structural Techniques

The structural data of the image you're utilizing is required by these methods. Pixels, colour distribution, distributions, pixel density, histograms, and other pertinent data are all included. The structural data for the region you're separating from the image must then be available.

You'll need that data to identify the location with your algorithm. The region based segmentation is followed by the algorithms we utilize in these implementations.

Also Read: 17 Courses on Drone Photography To Pursue Right Now

Stochastic Techniques

Rather than the structure of the necessary segment of the image, these methods demand information about the image's discrete pixel values. As a result, they don't need a lot of data to accomplish image segmentation and are effective when working with many photos. ANN algorithms and K-means clustering are examples of machine learning algorithms.

Hybrid Techniques

These algorithms employ both stochastic and structural approaches, as implied by their name. This means that when performing image segmentation, they utilize the structural information of the needed region as well as the discrete pixel information of the entire image.

Also Read: 15 + Courses That Will Help You Ace Food Photography

Image Segmentation Techniques

Now that we've learned about the various approaches and strategies for picture segmentation, we can get into the details. The main types of picture segmentation techniques are as follows:

1. Thresholding in Image processing Segmentation

2. Edge-Based Segmentation

3. Region-Based Segmentation

4. Watershed Segmentation

5. Clustering-Based Segmentation Algorithms

6. Neural Networks for Segmentation

Let’s discuss each one of these techniques in detail to understand their properties, benefits, and limitations:

1. Thresholding in image processing

The most fundamental image processing segmentation method is the threshold approach. It compares the intensity of each pixel to a predefined value to distinguish pixels in a picture (threshold). When the desired object is brighter than the background, it comes in helpful (unnecessary parts). The threshold value (T) can be thought of as a constant, but it will only work if the image has very little noise (unnecessary information and data). You can set the threshold value static or dynamic, depending on your needs.

A grayscale image is divided into two segments and converted to a binary image using the thresholding method (required and not required sections). Based on the different threshold values, we can classify thresholding segmentation in the following categories:

Also Read: 15+ Courses for Aspiring Wedding Photographer to Pursue Right Now

2. Simple Thresholding in image processing

The pixels in the image are either replaced with white or black in this procedure. If a pixel's intensity is shorter than the threshold value, it will be replaced with black. If it's more than the threshold value, though, white would be used instead. This is a simple thresholding method that is ideal for picture segmentation novices.

3. Otsu’s Binarization

Choosing a set threshold value and applying it to picture segmentation is simple thresholding. But how can you know if the value you selected was correct? While testing different values and choosing one is the simplest method, it is not the most efficient way. Consider an image with two peaks: one for the backdrop and one for the foreground.

The estimated amount of the midpoint of those peaks can be used as your threshold value utilizing Otsu binarization. If the image is bimodal, Otsu binarization uses the histogram to derive the threshold value.

This method is widely used for document scanning, pattern recognition, and file colour removal. It does, however, have several flaws. It won't work with non-bimodal photos (pictures whose histograms have different peaks).

Also Read: 15 Online Courses on Real Estate Photography For A High-Paying Career

4. Adaptive Thresholding

Having a single consistent threshold value may not be the best technique for every image. Different backdrops and settings alter the qualities of different photographs.

As a result, rather than employing a single constant threshold value to segment the entire image, you can keep the threshold value flexible. Different threshold settings will be kept for different regions of an image with this technique.

With photographs with shifting lighting conditions, this strategy works nicely. You'll need an algorithm that divides the image into smaller portions and calculates the threshold value for each.

Also Read: Top 27 courses on commercial photography on Udemy

5. Edge-Based Segmentation

One of the most used approaches of image processing segmentation is edge-based segmentation. It focuses on recognising the edges of various things in a photograph. This is an important step since edges hold a lot of information that can be used to help you locate the features of the various items in the image.

Edge detection is commonly used because it aids in the removal of unwanted and unneeded data from images. It significantly reduces the image size, making analysis much easier.

Edge-based segmentation algorithms look for edges in an image based on gray level, colour, texture, saturation, contrast, and other factors. Connecting all of the edges into edge links that more closely resemble the image borders will increase the quality of your output. There are a plethora of edge-based segmentation techniques to choose from. There are two types of them:

Also Read: 16 Courses on Mobile Photography for Beginners to Become a Pro

6. Search-Based Edge Detection

Using a computed estimate of the edge's local orientation, search-based edge detection algorithms search for local directional maxima of the gradient magnitude.

7. Zero-Crossing Based Edge Detection

Edge detection approaches based on zero crossings search for zero crossings in a derivative expression derived from the image.

To reduce undesirable noise and make it easier to recognise edges, you'll usually need to pre-process the image. Here are sSome of the best edge detection operators include Canny, Prewitt, Deriche, and Roberts cross. They make it easier to spot edges and detect discontinuities.

The purpose of edge-based detection is to achieve a partial segmentation minimum that allows you to group all local edges into a binary image. The edge chains in your newly formed binary picture must match the elements of the concerned image.

Also Read: Top 15+ Photography Courses For Beginners

8. Region-Based Segmentation

The image is divided into sections with comparable homes using region-based segmentation methods. The method finds these groups by first selecting a seed point, which could be a small chunk or a huge portion of the input image.

A region-based segmentation algorithm would either add additional pixels to the seed points or decrease them in order to combine them with other seed points after discovering them.

Based on these two methods, we can classify region-based segmentation into the following categories:

Region Growing

This approach starts with a limited collection of pixels and afterwards merges more pixels iteratively based on specific similarity parameters. A region-growing algorithm would start by selecting an arbitrary seed pixel in the image, comparing it to neighboring pixels, and then enlarging the region by identifying matches to the seed point.

When a region can no longer grow, the algorithm selects a new seed pixel that may or may not belong to any preexisting region. When a region has too many qualities, it can take up the majority of the image. To avoid making this mistake, region-growing algorithms create numerous regions at once.

For photos with a lot of noise, you should utilize region expanding algorithms instead of thresholding in image processing techniques because the noise makes it difficult to locate edges.

Region Splitting and Merging

A region splitting and merging focused approach, as the name implies, combines two actions: separating and merging regions of a picture.

It would divide the image into sections with similar characteristics and combine adjacent portions that are comparable. The method in region splitting considers the entire image, whereas in region growth, the algorithm focuses on a single location.

A divide-and-conquer approach is used in the region splitting and merging procedure. It separates the image into various parts and then matches them based on specified criteria. Split-merge algorithms are another name for the methods that accomplish this goal.

Also Read: 15 Courses on Adobe Lightroom to Upgrade Your Photography Career

9. Watershed Segmentation

A grayscale image is transformed using a watershed in image processing. It is a drainage divide or a geological watershed. The image would be treated like a topographic map using a watershed algorithm. It takes a pixel's brightness as its height and searches for the lines going along the tops of the ridges.

Watershed has a wide range of technical definitions and applications. It involves defining basins (the inverse of ridges) and flooding the basins with markers till they reach the watershed lines running through the ridges.

Because basins have many markers and ridges do not, the image is separated into various sections based on the 'height' of each pixel.

Every image is converted into a topographic map using the watershed method. The gray values of the pixels in the watershed segmentation method will reflect the terrain.

A landscape with slopes and hills, on the other hand, would undoubtedly have three-dimensional features. The watershed would take into account the image's three-dimensional representation and produce "catchment basins" as a result.

It has a wide range of medical applications, particularly medical imaging and MRI. Watershed segmentation is an essential aspect of medical picture segmentation, so if you want to work in that field, you should study it thoroughly.

10. Clustering-Based Segmentation Algorithms

Clustering algorithms should be familiar to anyone who has studied classification algorithms. They are unsupervised algorithms that aid in the discovery of hidden data in images that would otherwise be invisible to the naked eye. Clusters, structures, shadings, and other hidden data are included in this data.

Clustering algorithms partition a picture into clusters (disjoint groupings) of pixels with similar characteristics, as the name implies. It would divide the data into clusters, with the items in one cluster being more similar to those in the others.

K-means, Fuzzy c-means (FCM), and enhanced k-means algorithms are among the most common clustering techniques. The k-means clustering algorithm is frequently used in picture segmentation because it is simple and effective. The FCM algorithm, on the other hand, divides pixels into groups based on their degree of membership.

The most important clustering algorithms for segmentation in image processing are:

K-means Clustering

The unsupervised machine learning algorithm K-means is basic. It uses a set of clusters to categorize an image. It begins by splitting the image space into k pixels, each of which represents one of the k group centroids.

Then, depending on the distance between every object and the centroid, they allocate each object to a group. The centroids can be moved and reassigned once the algorithm has allocated all pixels to all clusters.

Fuzzy C-Means

The pixels in a picture can be clustered in several clusters using the fuzzy c-means clustering algorithm. This means that a pixel can belong to many clusters. Every pixel, on the other hand, would have varied degrees of similarity with each cluster. The optimization function in the fuzzy c-means method influences the correctness of your results.

The majority of your picture segmentation demands can be met using clustering methods. Check out this guide to learn more about clustering and the various kinds of clustering algorithms.

Neural Networks for Segmentation

You may not want to accomplish everything on your own. Perhaps you'd like an AI to handle the majority of your jobs, which neural networks for picture segmentation can absolutely do.

You'd use AI to examine an image and identify its many elements, such as objects, text, faces, and so on. Convolutional Neural Networks (CNNs) are widely used for image segmentation since they can swiftly recognise and interpret picture input.

Mask R-CNN is a deep learning framework developed by Facebook Artificial Intelligence Research (FAIR) that can construct a pixel-wise mask for each and every object in an image. Faster R-CNN object detection technology has been improved. For every object in an image, the Faster R-CNN uses two pieces of information: bounding box coordinates and object class. You gain an extra phase in this process when you use Mask R-CNN. After doing segmentation, R-CNN generates an object mask.

You'd start by feeding the input image towards the ConvNet, which would then generate the image's feature map. The system then uses the region proposal network (RPN) to create object proposals with their objectness values based on the feature maps.

The Roi pooling layer is then applied to the suggestions in order to reduce them to a single size. The system then sends the proposals to the linked layer for classification and produces an output with bounding boxes for each object.

How to learn Segmentation in Image Processing

In image processing, segmentation is a large issue with many sub-sections. There's a lot to learn in this field, from diverse image segmentation approaches to algorithms. With so much land to cover, it's easy to become disoriented.

To tackle these challenges, we propose taking Artificial Intelligence certification courses. A course on picture segmentation and related fields would teach you the fundamentals as well as advanced ideas. You'll learn about image processing, computer vision and picture segmentation, as well as various machine learning techniques.

Top Providers offering Photography courses and certifications

Conclusion

So that’s about it. Image segmentation in digital image processing is unquestionably a difficult and complex subject. Each of the Image segmentation techniques we reviewed in this post has its own set of benefits and drawbacks. By being familiar with them, you will be able to determine when one should be used and when the other should be avoided. With all of the new skills, you can participate in different competitive platforms to test your skills in image segmentation in digital image processing and gain more experience.

After going over these Image segmentations for Image processing, you might want to look into other popular online certifications. Take a look at the list below, which includes online degrees, certification, diploma courses and so on.

• Online Certification Courses

• Online Degree and Diploma Courses

• Online Short Term Courses and Certifications

• View list of all Online Courses and Certifications

• Online Free Courses and Certifications

Also check Top Certification courses

For more exciting opportunities, check out top certifications in the following top Technology Trends.