Field of View Distortion Calibration for Machine Vision
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Precision machine vision inspection systems face measurement errors due to field of view (FOV) distortion, which can be significant even at sub-micron levels, limiting their accuracy.
Innovation Solution
A method and system for correcting FOV distortion by acquiring and analyzing multiple images of a calibration target at various locations and orientations within the FOV, determining distortion parameters to achieve congruence between images, and applying these parameters to correct measurement errors in subsequent operations, without requiring precisely fabricated calibration targets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional calibration methods using precisely fabricated calibration targets are used, then measurement accuracy can be improved, but manufacturing cost and complexity increase significantly
Solution Approach 1:
The patent uses a digital copy of the calibration target pattern rather than a physical precision target. The calibration target is imaged through the optical system, and the captured image data is used to compute distortion parameters. This eliminates the need for precisely fabricated physical calibration targets while maintaining measurement accuracy through computational methods.
Solution Approach 2:
The patent replaces the mechanical/physical calibration approach with a computational/optical approach. Instead of using physically fabricated precision targets, the system uses image processing and mathematical models to characterize and correct distortion, substituting mechanical precision requirements with computational algorithms.
2Measurement precision
If FOV distortion correction is implemented, then measurement repeatability improves to better than 1/10 pixel accuracy, but system complexity increases due to multiple image acquisition and analysis steps
Solution Approach 1:
The patent performs distortion characterization and parameter determination in advance through a calibration process. Multiple images of the calibration target are acquired and analyzed to compute distortion parameters before actual measurement operations. This preliminary calibration stores correction data that can be applied efficiently during subsequent measurements, reducing real-time computational complexity.
Solution Approach 2:
The patent transforms the calibration problem into parameter estimation. By modeling distortion as a set of mathematical parameters (radial distortion coefficients, center coordinates), the system converts complex geometric distortion correction into parameter fitting problems. This allows efficient correction through algebraic manipulation rather than complex iterative optimization during measurements.
Data Source
AI summary
A method characterizes field of view (FOV) distortion and may correct machine vision measurements accordingly. A plurality of distorted images are acquired, with the same calibration target at a plurality of spaced-apart locations within the FOV. The method analyzes the images and determines distortion parameters that can correct the distortions in the images such that features included in the calibration target pattern achieve a sufficient or optimum degree of congruence between the various images. Distortion parameters may be determined for each optical configuration (e.g., lens combination, magnification, etc.) that is used by a machine vision inspection system. The distortion parameters may then be used to correct FOV distortion errors in subsequent inspection and measurement operations.