Image Segmentation: Techniques Used to Classify Images

Over the last several years, technology has evolved to a point where computers can detect and understand visual images almost as well as our human brains. But of course, that’s only come about as a result of our significant interventions and the development of a process known as computer vision.

Computer vision relies on a technique called image segmentation. Without it, an image simply remains a standalone visual with little relation  to the machine. But with segmentation, computers can correctly label and understand the various elements an image contains and make sense of this data for later use.

Once segmented, the individual elements can be processed and assessed for users’ needs. Parts of an image can be divided using regions or by the objects that are present within the image. Once they’re broken apart, the image elements can be individually labeled and grouped  together as part of the larger image recognition process. This makes it easier for users to identify important details about the image as a whole, as well as each of the individual features it contains. 

Image segmentation has many uses across industries. For instance, you can track objects in real time under video surveillance that uses image segmentation. Visual elements like people or vehicles can be isolated within the bigger video to make it easier for security personnel to review information as it comes in and take action if necessary. 

Types of image segmentation models 

Most image segmentation falls into three categories based on the type of information that needs to be extracted from the image. 

• Instance segmentation, like object detection, focuses on detecting and segmenting specific objects within the image and breaking these apart from the overall background. Overlapping objects can be more easily segmented using this approach and it’s often used to identify and track individual objects within a picture.
• Semantic segmentation divides images according to the pixels in a given image.In other words, semantic segmentation groups objects based on how similar their pixels are to each other, while acknowledging that these objects are different from the background and other objects in the image. Every pixel in the image receives a label under this type of image segmentation.
• Panoptic segmentation. joins semantic and instance segmentation at the same time. Each pixel is labeled both by its class and by the kind of object it is. This type of image segmentation offers the most detailed level of detecting and analysis, so it’s useful when the computer model needs to be as specific as possible, as is the case with autonomous vehicles.

Source: SuperAnnotate

Image segmentation techniques 

Each type of image segmentation comes with its own techniques based on its strengths and applications. These are typically broken into two separate categories – traditional and deep learning techniques.

Traditional techniques 

Used for decades in the computer vision field, traditional techniques arise from algorithmic models and mathematical equations to identify common characteristics within images so that objects can be labeled appropriately. The most common techniques used under traditional frameworks are explained here.

• Region-based. Images can be divided into regions based on overlapping criteria, like color or texture. In this technique, pixels are split and clustered according to similar features. Pixels close to each other are usually part of the same object, so the machine will look for similarities and differences in these areas to find the edges of objects.
• Edge detection. Building off the  region-based technique, edge detection focuses on places where pixels that border each other suddenly change.  Drastic pixel shifts often indicate where there might be a boundary to an object, so these areas are marked for review to delineate where the edge of that particular object is.
• Thresholding. The simplest form of image segmentation, thresholding splits pixels according to their classes and intensity. Most images are changed to grayscale to make this technique easier because the machine looks for areas of high and low contrast to divide objects. Binary images are produced during the segmentation process, creating contours that make it easier for the machine to distinguish between objects.
• Clustering. In this case, pixels are grouped into their own segments or clusters according to likeness. Each cluster  represents a similarity or common characteristic. 

Deep learning techniques 

Image segmentation in deep learning stands out as one of the best ways to get an accurate output, particularly when working with large scale, complex datasets. The two most popular techniques for this are detailed here.

• U-net, built to resemble a “U” shape, uses a process of upsampling and downsampling that operates much the same as an encoder and decoder in image captioning models. The level-based process of U-net is used to avoid as much data and information loss as possible throughout the segmentation process, making it one of the most accurate methodologies currently available.
• Mask R-CNN is a two-stage process that uses  a convolutional neural network, but delivers a high level of flexibility. Step one of this technique is to break the image down into proposed regions of interest within the overall visual. From here, step two is boxing, classifying, and applying binary masks to the separate regions and analyzing them piece by piece.

Industries that use image segmentation 

The ability to analyze information in images after it’s been extracted using segmentation profoundly improves workflows and procedures for a number of different industries.

Medical imaging and research 

MRIs, CT scans, X-rays, and other types of medical imaging all use a form of image segmentation to look for irregularities in patient scans. Image segmentation outranks many of the other ways  medical professionals diagnose and treat patients. 

For instance, healthcare workers can more easily  detect tumors with these tools. Image segmentation tools highlight the exact size and location of tumors on medical scans by separating these objects from healthy tissue. Brain scans also work in a similar way: image segmentation can separate different tissues within the brain to help doctors diagnose issues like Alzeimer’s disease or strokes, or planning for brain surgery.

Image segmentation can also be implemented for biomedical research, including tissue analysis, cell counting, and anatomical structure studies.

Autonomous vehicles 

Self-driving vehicles need a set of digital eyes to guide them. Image segmentation tools allow autonomous vehicles to perceive the world around them so they can avoid pedestrians and other cars, stay in the correct lane, and obey road signs. These crucial safety features make it possible for autonomous vehicles to use our roads.

Additional applications cover object recognition beyond  the standards expected on the road and detection of anomalies that could hurt the car’s drivability.

Satellite imaging 

Satellites can be used for all kinds of purposes, largely those that are difficult or impossible for humans to complete on their own. Monitoring large areas of land, for example, only happens because of aerial-based satellites and their ability to view hundreds of miles of land at one time.

With image segmentation, these satellites can more accurately monitor for environmental changes that require action to be taken, in much the same way that farmers monitor their crops with image segmentation. They can also be used for extensive urban planning projects, particularly where rural or agricultural land is being redeveloped into residential, retail, or work space.

Gaming 

As games become more interactive, the technology needed to power them becomes more complex. Image segmentation has paved the way for users to interact as if they’re part of the games themselves – especially in virtual reality environments – in new, immersive ways. Characters can also interact with game elements in new ways, providing an enhanced gameplay experience for users.

Robotics 

Our daily lives increasingly involve the presence of robots and the use of AI tools to manage them is also growing., As part of a wider practice of image segmentation,Object recognition for robots empowers  them to understand and interact with their environment. This helps them follow commands in a precise way, particularly when they need to identify objects in an unfamiliar environment.

Image segmentation also helps robotic navigation, i.e., moving the robot from one point to another. Combined with object segmentation, robots can interact with different elements in their environment and make decisions on their own such as planning a navigation path and avoiding obstacles in their way.

Challenges with image segmentation 

No matter how complex and well-built your machine becomes, AI stays at the mercy of good training data. Without it, you run the risk of inaccurate results and long re-training periods in an effort to correct mistakes made upfront. There are other important challenges to be aware of when it comes to image segmentation, including:

• Image ambiguity. Not every image you feed into the machine has crisp, clear boundaries that make it easy for the algorithm to distinguish and segment objects. Regions with similar characteristics or lighting variations and other noise-based issues can have a significant impact on the accuracy of the segmentation.
• Over and under segmentation. Images can be divided into too many regions, also known as over segmentation. On the other hand, multiple groups could be clustered together as a single region, aka under segmentation. There’s a fine line in balancing these, and even more so when dealing with small objects in multiple points of an image.
• Resource drain. Powering AI-based machinery takes more energy than you would think. The computer resources needed to launch and maintain deep learning models can quickly become complex, so ensuring that you have everything you need to allow for real-time segmentation can be a challenge.

Don't split hairs - split images! 

Image segmentation is a vital part of Computer vision relies on image segmentation to build the many applications that machines provide in our lives, most noticeably when using deep learning models to replicate human behavior. This powerful technique makes identifying and understanding objects within an image quicker and easier – an essential feature in many industries.

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