Research
Usami Laboratory works on image processing, computer vision, and machine learning through four research themes.
Research Framework
Our research is organized around two connected directions: understanding principles and building applications.
We develop fundamental technologies for extracting, understanding, and using information from images and videos, while applying them to medicine, manufacturing, infrastructure, and real-world problems.
We value not only accuracy, but also why a method works, where it fails, and how it can be evaluated in a reproducible and useful way.
Principles x Applications
The laboratory combines foundational modeling with applied research for real-world deployment.
Principle-oriented research
Image understanding grounded in optics, physics, geometry, and mathematics.
- Physics-based image analysis
- Spatiotemporal modeling
- Generative model evaluation
Application-oriented research
Technical development for concrete problems in medicine, industry, and society.
- Medical image diagnosis support
- Visual inspection
- Action recognition systems
Research Themes
The laboratory currently develops projects around four major themes.
Medical Image Analysis, 3D Shape Reconstruction, and Diagnostic Support
Research on lesion detection, segmentation, 3D shape reconstruction, quantitative measurement, and diagnostic support using endoscopy, CT, cellular, and pathological images.
Physics-based Vision
Research on estimating shape, material, reflectance, and illumination from images by modeling reflection, transmission, scattering, and other optical phenomena.
Real-world Sensing and Spatiotemporal Modeling
Research on extracting temporal and spatial structure from videos and sensor data for action recognition, forecasting, and anomaly detection.
Generative AI, Learning Support, and Validation
Research on generative models, learning support, data augmentation, automatic question generation, and validation of generated outputs.
Evaluation Philosophy
We evaluate research outcomes from multiple viewpoints, not only by a single quantitative metric.
- Reproducibility: clear experimental conditions and traceable evaluation. - Generalization: robustness beyond a single dataset or imaging condition. - Interpretability: understanding why a method works and where it breaks. - Social impact: whether the result creates value in actual workflows.