Machine Vision Calibration for Opto-Fluidic Instrument Positioning
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Solution Overview
Problem
Current machine vision systems are inadequate for calibrating and positioning components in opto-fluidic instruments used for imaging biological specimens, as they lack the precision and reliability required for state-of-the-art imaging systems.
Innovation Solution
A method and system that determine 3D positions of objects within a reference coordinate system using stereo-images, generate transformation matrices, and calibrate motion control systems to achieve precise positioning and co-registration of instrumental components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If off-the-shelf components and existing solutions are used, then device complexity is reduced, but manufacturing precision and measurement precision are insufficient for state-of-the-art imaging systems
Solution Approach 1:
The machine vision system is divided into multiple independent modules: stereo-image acquisition module, 3D position determination module, transformation matrix generation module, and motion control calibration module. Each module performs a specific function, allowing for precise positioning while maintaining manageable system complexity through modular design.
Solution Approach 2:
The system transitions from 2D image coordinates to 3D physical space coordinates through stereo-vision triangulation and transformation matrices. This dimensional transformation enables precise 3D positioning of instrument components by capturing depth information from two different camera angles and converting it into accurate spatial coordinates.
2Measurement precision
If a specifically designed machine vision system is implemented, then measurement precision improves, but device complexity increases
Solution Approach 1:
The machine vision system is designed to perform multiple functions: capturing stereo-images, determining 3D positions, generating transformation matrices, and calibrating motion control systems. This multi-functional approach achieves high measurement precision while reducing overall system complexity by consolidating multiple calibration and positioning functions into a single integrated system.
Solution Approach 2:
The system uses itself for calibration by capturing images of the instrument components, determining their 3D positions, and generating correction matrices that are automatically applied to calibrate the motion control systems. This self-calibration capability eliminates the need for external complex calibration equipment and procedures.
3Measurement precision
If stereo-image acquisition at fixed distance is used, then measurement precision improves, but ease of operation decreases
Solution Approach 1:
The system performs preliminary calibration by acquiring stereo-images at a fixed distance to establish transformation matrices before actual positioning operations. This pre-calibration step creates a reference coordinate system that simplifies subsequent positioning operations, as the system already has the transformation data needed to convert image coordinates to physical positions without requiring manual adjustment during operation.
Data Source
AI summary
A machine vision system for calibrating and/or positioning of various motion control modules of an opto-fluidic instrument/tool/instrument having integrated optics and fluidics modules configured for imaging of biological specimens is disclosed. The machine vision system includes determining 3D positions of an object within a reference coordinate system based on a plurality of stereo-images comprising the object; generating a transformation matrix based on the determined 3D positions of the object with respect to a reference position; and calibrating one or more motion control systems based on the determined transformation matrix. The methods also include determining a 3D position of an object within a reference coordinate system based on a stereo-image of the object; generating a 3D offset value between the determined 3D position and a reference location of the object; updating the 3D position using the 3D offset value; and positioning the object based on the corrected 3D position.