Microscope Scene Recognition Stabilization
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Solution Overview
Problem
Existing microscope systems face challenges in accurately automating device settings for different scenes due to the need for extensive learning data and time, particularly with advanced functions, leading to potential hunting in inference results and frequent setting changes that hinder observation.
Innovation Solution
A microscope system that includes a scene recognition unit using a machine learning model to identify scenes, a determination unit to stabilize the recognition results over time, and a setting unit to adjust imaging settings based on the determined scene, thereby ensuring stable and appropriate device settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If a learning model is used to automatically determine device settings based on live images, then automation of device settings is improved, but inference accuracy deteriorates leading to hunting and frequent setting changes
Solution Approach 1:
The system performs preliminary scene recognition on a captured image before using it for setting determination. By pre-processing the image through scene recognition, the system prepares more reliable input data for the learning model, thereby improving inference accuracy and reducing hunting phenomena while maintaining automation.
Solution Approach 2:
The patent introduces an intermediate processing step where captured images are first subjected to scene recognition to generate scene information, which then serves as input for the learning model. This intermediary process refines the input data quality, enabling more accurate and stable setting determinations without reducing automation.
2Adaptability or versatility
If various device settings are supported in the microscope system, then adaptability to different scenes is improved, but the amount of learning data and time required increases
Solution Approach 1:
The system segments the scene recognition process into distinct stages: capturing an image, performing scene recognition to identify scene type, and then determining device settings based on the recognized scene. This segmentation allows the use of pre-trained scene recognition models for common scene types, reducing the need for extensive custom learning data while maintaining adaptability to various microscopy scenarios.
Data Source
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AI summary
A microscope system includes: a microscope; a digital camera configured to image a sample through the microscope; a scene recognition unit configured to perform scene recognition based on an image of the sample obtained by the digital camera, using a machine learning model that has learned a plurality of scenes; a scene determination unit configured to perform scene determination based on a recognition result of the scene recognition unit; a stabilization unit configured to temporally stabilize a determination result of the scene determination unit; and a setting unit configured to change settings of the digital camera based on the determination result of the scene determination unit.