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.
- Things: Things refer, to countable objects or distinct entities in an image with clear boundaries, like people, flowers, cars, animals etc. So, the segmentation of “Things” aims to label individual pixels in the image to specific classes by delineating the boundaries of individual objects within the image
- Stuff: Stuff refers to specific regions or areas in an image different elements in an image like background or repeating patterns of similar materials which can not be counted like road, sky and grass which may not have clear boundaries but play a crucial role in understanding the overall context in an image. The segmentation of “Stuff” involves grouping of pixels in an image into clearly identifiable regions based on the common properties like colour, texture or context.
Explain Image Segmentation : Techniques and Applications
Image segmentation is one of the key computer vision tasks, It separates objects, boundaries, or structures within the image for more meaningful analysis. Image segmentation plays an important role in extracting meaningful information from images, enabling computers to perceive and understand visual data in a manner that humans understand, view, and perceive. In this article let us discuss in detail image segmentation, types of image segmentation, how image segmentation is done, and its use cases in different domains.
Table of Content
- What is Image Segmentation?
- Why do we need Image Segmentation?
- Image segmentation vs. object detection vs. image classification
- Semantic Classes in Image Segmentation: Things and Stuff.
- Semantic segmentation
- Instance segmentation
- Panoptic segmentation
- Traditional image segmentation techniques
- Deep learning image segmentation models
- Applications of Image segmentation
- Conclusion: