Machine-Learning Optical Lens Calibration for Depth-of-Field Precision
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Solution Overview
Problem
Conventional methods for determining the depth of field in optical lens systems are time-consuming and prone to inaccuracies, often requiring manual adjustments and limited to predetermined aperture settings, lacking automation and precision in focus and aperture mechanisms.
Innovation Solution
A computer-controlled system that automatically adjusts focus and aperture settings using a machine learning model to optimize decode performance by iteratively capturing images of a target with varying settings, storing associations in a database, and selecting optimal settings based on decode performance metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual adjustment methods are used for determining depth of field, then the process can be performed with simple equipment, but the determination is time-consuming and prone to inaccuracies
Solution Approach 1:
The patent replaces manual mechanical adjustment of focus and aperture with an automated computer-controlled system. The computer automatically adjusts the focus mechanism and aperture mechanism while capturing images at multiple settings, eliminating manual intervention and significantly reducing calibration time while improving measurement precision through systematic automated control.
Solution Approach 2:
The system performs self-calibration by automatically determining optimal focus and aperture settings without requiring manual operation. The computer-controlled system independently adjusts parameters, captures images, analyzes results, and determines depth of field measurements autonomously, making the calibration process self-service oriented.
2Productivity
If automated computer-controlled adjustment is implemented, then calibration speed and precision improve, but device complexity increases
Solution Approach 1:
The computer-controlled system serves multiple functions: it controls the focus mechanism, controls the aperture mechanism, captures images, stores image data, and performs analysis to determine depth of field. By consolidating these multiple functions into a single integrated system, the patent achieves high productivity while managing device complexity through multi-functionality.
3Measurement precision
If iterative image capturing with varying settings is performed, then optimal settings are identified through learning, but the number of image captures and processing steps increases
Solution Approach 1:
The system implements feedback by capturing images at multiple focus and aperture settings, analyzing the decode performance of each captured image, and using this analysis to determine optimal settings. The computer processes the series of images, compares performance metrics, and identifies the settings that yield the best decode performance, creating a closed-loop feedback system that improves measurement precision.
Data Source
AI summary
Techniques for optical lens characterization and calibration are described herein. In an example, a computer system receives a first image of a target captured by an image acquisition system having a camera and a lens and using first setting values in a setting space. The computer system inputs the first setting values and a first decode performance associated with image acquisition system having the first setting values for the barcode sets in the first image into a machine learning model and determines a representation of the setting space. The computer system inputs the machine learning model and one or more conditions into an algorithm and receives an output indicating second setting values in the setting space for the image acquisition system. The computer system sends the second setting values to the controller, which is configured to set the settings for the image acquisition system based on the second setting values.