Semantic Classes in Image Segmentation: Things and Stuff.

What is Image Segmentation?

Image segmentation is a fundamental technique in digital image processing and computer vision. It involves partitioning a digital image into multiple segments (regions or objects) to simplify and analyze an image by separating it into meaningful components, Which makes the image processing more efficient by focusing on specific regions of interest. A typical image segmentation task goes through the following steps:...

Why do we need Image Segmentation?

Image segmentation is crucial in computer vision tasks because it breaks down complex images into manageable pieces. It’s like separating ingredients in a dish. By isolating objects (things) and backgrounds (stuff), image analysis becomes more efficient and accurate. This is essential for tasks like self-driving cars identifying objects or medical imaging analyzing tumours. Understanding the image’s content at this granular level unlocks a wider range of applications in computer vision....

Image segmentation vs. object detection vs. image classification

The comparison between Image segmentation, object detection and image classification are as follows:...

Semantic Classes in Image Segmentation: Things and Stuff.

In semantic image segmentation, we categorize image pixels based on their semantic meaning, not just their visual properties. This classification system often uses two main categories: Things and Stuff....

Semantic segmentation

Semantic Segmentation is one of the different types of image segmentation where a class label is assigned to image pixels using deep learning (DL) algorithm. In Semantic Segmentation, collections of pixels in an image are identified and classified by assigning a class label based on their characteristics such as colour, texture and shape. This provides a pixel-wise map of an image (segmentation map) to enable more detailed and accurate image analysis....

Instance segmentation

Instance segmentation in image segmentation of computer vision task is a more sophisticated feature which involves identifying and delineating each individual object within an image. So instance segmentation goes beyond just identifying objects in an image, but also delineate the exact boundaries of each individual instance of that object....

Panoptic segmentation

Panoptic segmentation goes a step further in image segmentation of computer vision tasks, by combining the features and processes of semantic and instance segmentation techniques. So the panoptic segmentation algorithm creates a comprehensive image analysis by simultaneously classifying every pixel and identifying distinct object instances of the same class....

Traditional image segmentation techniques

The traditional image segmentation techniques which formed the foundation of modern image segmentation methods using deep learning algorithms, uses thresholding, edge detection, Region-Based Segmentation, clustering algorithms and Watershed Segmentation. These techniques are more reliant on principle of image processing, mathematical operation and heuristics to separate an image into meaningful regions....

Deep learning image segmentation models

Deep learning image segmentation models are a powerful technique which leverages the neural network architecture to automatically divide an image into different segments and extract features from images for accurate analysis and segmentation tasks....

Applications of Image segmentation

Below are the list of different uses cases of Image Segmentation in Image processing:...

Conclusion:

In this article about Image Segmentation in image process, we have discussed about one of the key computer vision tasks and how this process helps image processing and analysis in many different fields including medical image analytics for diagnosis and planning better treatment methods. Also this article delves into the traditional image segmentation models over how advanced deep learning models are used today in image processing and segmentation tasks....