상세 보기
Training-Free White Blood Cell Detection: Achieving State-of-the-Art Performance with Traditional Computer Vision
- Baek, SeungBin;
- Shin, ManJae;
- Kim, SungMin
Citations
SCOPUS
0초록
This study presents a training-free approach for white blood cell (WBC) detection using traditional computer vision on the BCCD dataset. While deep learning methods dominate blood cell analysis, they require extensive training data and computational resources, limiting clinical adoption. Our methodology combines HSV color space preprocessing, watershed segmentation, and morphological operations to achieve 99.7% mAP@0.5 with sub-6ms processing times. The approach eliminates training requirements while providing interpretable results suitable for clinical environments. Results demonstrate competitive performance compared to AI-based methods with significant advantages in deployment simplicity and computational efficiency. © 2025 IEEE.
키워드
BCCD dataset; HSV color space; traditional computer vision; training-free methods; watershed segmentation; white blood cell detection
- 제목
- Training-Free White Blood Cell Detection: Achieving State-of-the-Art Performance with Traditional Computer Vision
- 저자
- Baek, SeungBin; Shin, ManJae; Kim, SungMin
- 발행일
- 2025
- 유형
- Conference paper
- 저널명
- 2025 7th International Conference on Robotics and Computer Vision (ICRCV)
- 페이지
- 1 ~ 6