What is Semantic Segmentation?
Semantic segmentation is a process in computer vision that involves classifying each pixel in an image into a predefined category. It aims to provide an understanding of the scene at a more granular level compared to traditional image classification, which only assigns a single label to an entire image.
Key Aspects
- Pixel-level Classification: Each pixel corresponds to a specific class label, allowing for detailed insights into the image structure.
- Applications: Used extensively in areas like autonomous driving, medical imaging, and object detection.
- Techniques: Techniques include convolutional neural networks (CNNs) and advanced architectures like U-Net and SegNet, specifically designed for segmentation tasks.
Real-World Examples
In autonomous vehicles, semantic segmentation helps identify roads, pedestrians, and obstacles, enhancing safety and navigation. In healthcare, it assists in isolating tumors in medical images, facilitating precise treatment planning.
Conclusion
Overall, semantic segmentation is a foundational technique in deep learning for computer vision applications, providing essential capabilities that enable machines to interpret and understand images similarly to human perception.