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.
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: