Imaging Device Calibration via Machine Learning Transformation
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Solution Overview
Problem
Imaging devices generate inconsistent output images due to environmental and hardware differences, making it difficult and time-consuming to programmatically adapt each device to produce expected images from input signals.
Innovation Solution
A computing system generates a separate transformation model for each imaging device using machine learning, trained to transform images to resemble those produced by a properly calibrated reference image generator, ensuring consistency across devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional calibration methods are used for each imaging device, then device-specific calibration parameters can be adjusted, but it is difficult and time-consuming to programmatically adapt each image generator to produce expected output images
Solution Approach 1:
The system performs preliminary calibration by capturing reference images of a calibration object with multiple imaging devices under controlled conditions before actual use. These reference images are stored and used as a basis for automatic calibration, eliminating the need for time-consuming manual adjustment of each device during operation.
Solution Approach 2:
A central server acts as an intermediary between imaging devices and the calibration system. The server receives images from multiple devices, processes them to determine calibration parameters, and automatically adjusts device settings. This intermediary automates the calibration process, reducing manual intervention time while maintaining precision.
2Adaptability or versatility
If each imaging device is calibrated to its particular environment, then device-specific environmental conditions are compensated, but small differences in environment and hardware still result in images that differ from device to device
Solution Approach 1:
The calibration system creates a universal reference frame that works across all imaging devices regardless of their specific environmental conditions or hardware variations. By capturing reference images from multiple devices and processing them centrally, the system establishes a common calibration standard that ensures consistent output images across the entire network of devices.
Solution Approach 2:
The system uses feedback from captured reference images to automatically adjust calibration parameters. The server analyzes the reference images, determines the appropriate calibration parameters, and applies them to the imaging devices. This closed-loop feedback mechanism ensures that environmental and hardware variations are compensated, achieving consistent images across all devices.
3Measurement precision
If manual calibration adjustment is performed for each device, then image accuracy can be improved, but the process is time-consuming and difficult to implement programmatically
Solution Approach 1:
The imaging devices and calibration system perform self-calibration without requiring manual intervention. The system automatically captures reference images, processes them to determine calibration parameters, and applies the calibration settings. This self-service approach simplifies the calibration process while maintaining high image accuracy across all devices.
Solution Approach 2:
The system replaces manual mechanical calibration adjustments with automated computational processing. Instead of physically adjusting device parameters by hand, the server uses image processing algorithms to calculate calibration parameters and automatically configures the devices programmatically, reducing complexity and improving accuracy.
Data Source
AI summary
A method comprises: obtaining a current initial image generated by an image generator of an imaging device based on current input signals of sensors of the imaging device; and applying a transformation model to the current initial image to generate a current transformed image, wherein the transformation model is a machine-learning model that has been trained to generate transformed images that more closely resemble reference images generated by a reference image generator.