Advances in Deep Learning for Semantic Segmentation of Low-Contrast Images: A Systematic Review of Methods, Challenges, and Future Directions
The semantic segmentation (SS) of low-contrast images (LCIs) remains a significant challenge in computer vision, particularly for sensor-driven applications like medical imaging, autonomous navigation, and industrial defect detection, where accurate object delineation is critical. This systematic re...
Saved in:
| Main Authors: | Claudio Urrea, Maximiliano Vélez |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-03-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/25/7/2043 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Pseudolabel guided pixels contrast for domain adaptive semantic segmentation
by: Jianzi Xiang, et al.
Published: (2024-12-01) -
PSNet: Patch-Based Self-Attention Network for 3D Point Cloud Semantic Segmentation
by: Hong Yi, et al.
Published: (2025-06-01) -
A dual attention mechanism semantic segmentation method for autonomous driving
by: WANG Yannian, et al.
Published: (2023-12-01) -
Mitigating Class Confusion in Class-Incremental Semantic Segmentation
by: Nayoung Ko, et al.
Published: (2025-01-01) -
Insights of semantic segmentation using the DeepLab architecture for autonomous driving
by: Javed Subhedar, et al.
Published: (2025-06-01)