Automated Sample Support Grid Recognition by Image Matching
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Solution Overview
Problem
Existing methods for identifying and verifying sample support grids in scientific instruments are manual, subjective, error-prone, and lack automation, leading to misidentification and increased imaging and measurement errors during material analysis.
Innovation Solution
An automated system that captures images of sample supports, compares non-uniformities, and matches them to known profiles to verify the correct sample support grid using a computer-executable process, reducing reliance on manual input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual methods are used to identify and verify sample support grids, then the process is simple to implement, but the reliability and accuracy of sample support recognition deteriorates leading to misidentification and imaging errors
Solution Approach 1:
The patent replaces manual visual inspection and mechanical identification methods with an automated imaging and image processing system. The system uses captured images of sample supports, processes them through computer-executable instructions, and automatically identifies and verifies sample support grids, eliminating human subjectivity and error while maintaining operational simplicity through software automation.
2Productivity
If automated image capture and matching processes are implemented, then productivity and recognition accuracy improve, but device complexity increases
Solution Approach 1:
The system performs self-verification by automatically capturing images of sample supports, processing these images through built-in computer-executable instructions, and identifying sample support grids without requiring external manual intervention. The automated matching process compares captured images against reference images stored in the system, enabling the device to self-validate sample support identity and eliminate the need for separate verification steps.
3Measurement precision
If manual verification of sample supports is performed, then device complexity remains low, but measurement precision and sample placement accuracy deteriorate
Solution Approach 1:
The system implements a feedback mechanism where captured images of sample supports are processed and compared against reference images stored in memory. The computer-executable instructions analyze the captured images, identify sample support grids, and provide verification feedback to confirm correct sample support placement. This automated feedback loop ensures high measurement precision by objectively verifying sample support identity before analysis proceeds.
Data Source
AI summary
Embodiments herein relate to a process for sample support recognition. A system can comprise a memory that stores, and a processor that executes, computer executable components. The computer executable components can comprise an imaging component that captures an image of an unknown sample support comprising a material layer; and a matching component that matches the unknown sample support to a known sample support based on an unknown non-uniformity profile comprising one or more non-uniformities of the material layer in the image of the unknown sample support.