Semantic segmentation is applied in scenarios requiring pixel-level understanding of images. In autonomous vehicles, it is used to identify and differentiate between road elements, such as lanes, pedestrians, and vehicles. In medical imaging, semantic segmentation helps in identifying regions of interest, such as tumors or organs, in X-rays or MRI scans. Other applications include agriculture (e.g., plant and soil segmentation), environmental monitoring (e.g., land-use classification), and video analytics (e.g., activity recognition). The ability to assign meaningful labels to each pixel makes semantic segmentation valuable in diverse domains.
Where do you apply the concept of 'semantic segmentation'?
- Exploring Vector Database Use Cases
- Embedding 101
- Large Language Models (LLMs) 101
- How to Pick the Right Vector Database for Your Use Case
- Advanced Techniques in Vector Database Management
- All learn series →
Recommended AI Learn Series
VectorDB for GenAI Apps
Zilliz Cloud is a managed vector database perfect for building GenAI applications.
Try Zilliz Cloud for FreeKeep Reading
What is computer vision?
Computer vision is a field of artificial intelligence (AI) that focuses on enabling machines to understand and interpret
How does DeepSeek handle sensitive information in its AI models?
DeepSeek handles sensitive information in its AI models by implementing a combination of data protection measures, acces
How do edge AI systems ensure data integrity?
Edge AI systems ensure data integrity by implementing various strategies that focus on data validation, secure transmiss