The size of the global market for computer vision was estimated at USD 11.22 billion in
2021, and it is anticipated to increase at a 7.0% CAGR from 2022 to 2030. Computer vision
systems utilizing artificial intelligence (AI) are becoming more and more common in a range
of applications, such as consumer drones and fully or partially autonomous vehicles.

The Role of Computer Vision in Image Segmentation

Recent developments in computer vision, including image sensors, sophisticated cameras,
and deep learning methods, have increased the potential applications for computer vision
systems across a range of sectors. Sectors include education, healthcare, robotics, consumer
electronics, retail, manufacturing, and security & surveillance, among others.

The partition of a digital image into several segments (objects) is known as image
segmentation. Segmentation aims to transform an image’s representation into one that is
more meaningful and understandable. 

Various Image Segmentation Types

Based on the quantity and type of information they communicate, image segmentation
tasks can be divided into three groups: semantic, instance, and panoptic segmentation.  
Semantic segmentation (not instance-based)

The process of semantic segmentation, often referred to as non-instance segmentation, aids
in describing the location of the items as well as their form, size, and shape. 

It is primarily applied when a model needs to know for sure whether or not an image
contains an object of interest and which portions of the image do not. Without taking into
account any further information or context, pixels are simply labeled as belonging to a
certain class. 

Segmentation by Instance 

The practice of segmenting objects by their presence, position, quantity, size, and shape is
known as instance segmentation. With each pixel, the objective is to better comprehend the
image. 
To distinguish between objects that overlap or are similar, the pixels are categorized based
on “instances” rather than classes.

Pan-optic segmentation

Since it combines semantic and instance segmentation and offers detailed data for
sophisticated ML algorithms, panoptic segmentation is by far the most informative task. 

Popular Image Segmentations with Computer Vision in Various Sectors
Due to the complicated robotics tasks that self-driving cars must undertake and the
need for a thorough grasp of their environment, it is particularly well-liked in the field of
autonomous driving. Geosensing for mapping land use with satellite imaging, traffic
control, city planning, and road monitoring are further geospatial uses for semantic
segmentation. 

• Precision farming robotic initiatives are aided in real-time to start weeding by semantic
  segmentation of crops and weeds. With the use of these sophisticated computer vision
  systems, manual agricultural activity monitoring has been greatly reduced. 
• Semantic segmentation makes it possible for fashion eCommerce firms to automate
  operations like the parsing of garments that are traditionally quite difficult. 
• The recognition of facial features is another popular topic of study. By analyzing facial
  traits, the algorithms can infer gender, age, ethnicity, emotion, and more. These
  segmentation tasks get more difficult due to elements like various lighting conditions,
  facial expressions, orientation, occlusion, and image resolution. 

In the context of cancer research, computer vision technologies are also gaining ground
in the healthcare sector. When examples are used to identify the morphologies of the
malignant cells to speed up diagnosis procedures, segmentation is frequently utilized. 

Are you prepping to begin your Image Segmentation Use case? 

Reach out to our professionals in Data Labeler, so they can assist you in quickly and
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