Microscopic Object Acquisition TRIZ Cases
- Microscope Image Area Selection Using ML-Based Magnification Guidance
ML-based image analysis highlights microscope regions worth enlarging, reducing operator burden while improving examination accuracy.
- Microscope Focus Repositioning Using ML Signatures for Thick Samples
ML image signatures let microscopes detect focus drift and restore target structures in thick, time-variable samples without added hardware.
- Lensless Optical Detection With LED Failure Compensation
Uniform LED current control keeps sensor detection values in range, limiting luminance errors and preserving lensless imaging accuracy after LED failure.
- Rock Thin Section Mineral And Grain Boundary Detection With One DNN
A single DNN combines optical, XPL, and electron microscopy data to identify minerals and grain boundaries faster and more consistently.