Adaptive Camera Coordinate Frame for Microscopic Vision Calibration
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Solution Overview
Problem
Microscopic vision measurement precision is inferior due to low calibration precision, primarily caused by the strong nonlinearity of the optimization objective function, which is sensitive to initial values, especially in the narrow depth of field with inaccurate closed-form solutions.
Innovation Solution
A microscopic vision measurement method based on adaptive positioning of the camera coordinate frame, where the camera coordinate frames are moved along their optical axes, and a planar target is freely positioned to decrease nonlinearity, allowing for high-precision calibration and measurement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional calibration methods are used in microscopic vision measurement, then the measurement process can be completed, but the calibration precision is low due to strong nonlinearity and sensitivity to initial values
Solution Approach 1:
The patent transforms the conventional fixed camera coordinate frame into an adaptive movable coordinate frame along the optical axis. By introducing adjustable parameters (distance d between image plane and target plane, and coordinate frame position), the system changes the geometric parameters to achieve better calibration precision while managing the complexity through systematic parameter optimization.
Solution Approach 2:
The patent makes the camera coordinate frame dynamic rather than fixed. The coordinate frame can be adaptively positioned along the optical axis based on the actual depth of field and target position, allowing the system to dynamically adjust to different measurement scenarios and improve calibration accuracy across varying conditions.
2Measurement precision
If the camera coordinate frame is fixed in conventional microscopic vision measurement, then the system structure is simple, but the calibration precision is low due to narrow depth of field and inaccurate closed-form solutions
Solution Approach 1:
The patent separates the calibration process into distinct stages: first determining the optimal position of the camera coordinate frame along the optical axis, then performing the actual calibration. This segmentation allows each sub-problem to be solved independently, reducing the overall complexity while improving precision.
Solution Approach 2:
The patent performs preliminary positioning of the camera coordinate frame before conducting the main calibration process. By pre-determining the optimal coordinate frame position based on depth of field considerations, the system prepares the measurement system in advance, ensuring that subsequent calibration operations start from an optimal configuration.
3Measurement precision
If adaptive positioning of camera coordinate frame is implemented, then calibration precision and measurement precision are improved, but the device complexity increases
Solution Approach 1:
The patent implements a feedback mechanism where the calibration results and measurement data are used to evaluate and adjust the camera coordinate frame position. This closed-loop approach allows the system to automatically optimize its configuration, improving precision while the feedback control manages the complexity through iterative refinement rather than complex manual adjustments.
Data Source
AI summary
The present disclosure provides a microscopic vision measurement method based on the adaptive positioning of the camera coordinate frame which includes: calibrating parameters of a microscopic stereo vision measurement model (201); acquiring pairs of synchronical images and transmitting the acquired images to a computer through an image acquisition card (202); calculating 3D coordinates of feature points in a scene according to the matched pairs of feature points in the scene obtained from the synchronical images and the calibrated parameters of the microscopic stereo vision measurement model (203); and performing specific measurement according to the 3D coordinates of the feature points in the scene (204). With the method, the nonlinearity of the objective function in the microscopic vision calibration optimization is effectively decreased and a better calibration result is obtained.