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.
For example, all pixels related to a ‘tree’ would be labelled the same object name without distinguishing between individual trees. Another example would be, group of people in an image would be labelled as single object as ‘persons’, instead of identifying individual people.
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: