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Building a Modular Computer Vision Perception System: Part 4 — Image Segmentation
- Image segmentation is crucial for detailed scene understanding in perception systems, providing pixel-precise masks outlining object boundaries.
- Segmentation, particularly instance segmentation, is essential for applications like autonomous vehicles, robotics, and medical imaging.
- Compared to detection, segmentation offers a more detailed understanding of objects and their boundaries, enhancing scene analysis.
- The Segment Anything Model (SAM) is a significant advancement in segmentation technology, capable of segmenting diverse objects.
- SAM integrates well with detection results to generate accurate segmentation masks, following a workflow of detection → tracking → segmentation.
- Segmentation module enhances detection and tracking results by converting bounding boxes into precise masks, improving object boundaries.
- Segmentation masks refine detection results by accurately fitting bounding boxes to object shapes, especially for irregular objects.
- By extracting colors only from segmented object pixels, masks enable more accurate color analysis and distance estimation from depth maps.
- Optimizations for computational efficiency in segmentation models should be considered based on deployment environment requirements.
- Segmentation combined with depth estimation provides detailed 3D scene understanding, enabling applications like object modeling and collision detection.
- While powerful, segmentation has limitations, encouraging the combination with classifiers or using detection results for improved segment labeling.
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