Camera Calibration Assessment Using Segmented Quality Measures
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Camera calibration methods face challenges in accurately determining systematic errors and residual parameter uncertainties, requiring expert knowledge and complex experiments, which limits their usability by laypersons and affects the precision of subsequent processing steps.
Innovation Solution
A method that assesses camera calibration by providing two quality measures: a systematic error estimate and an uncertainty metric, allowing for the quantification of both systematic errors and residual uncertainties, enabling direct feedback and improvement of calibration processes without requiring empirical values or complex simulations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex control experiments and expert knowledge are used to assess camera calibration, then measurement precision improves, but device complexity and ease of operation worsen
Solution Approach 1:
The calibration assessment method enables the camera system to self-evaluate its calibration quality using automatically computed quality measures (first and second quality measures) derived from calibration images, eliminating the need for expert intervention and complex control experiments while maintaining high measurement precision
2Measurement precision
If complex control experiments and expert knowledge are used to assess camera calibration, then measurement precision improves, but ease of operation worsens
Solution Approach 1:
The system automatically computes quality measures and generates feedback reports without requiring expert knowledge or complex experimental procedures, making the calibration assessment process accessible and easy to operate for users while maintaining high measurement precision through rigorous mathematical evaluation
3Measurement precision
If traditional calibration assessment methods are used, then systematic error detection may improve, but productivity worsens
Solution Approach 1:
The method replaces complex mechanical control experiments and manual expert assessment with automated computational algorithms that calculate quality measures from calibration images, achieving both high systematic error detection accuracy and improved productivity through efficient computer-based processing
Data Source
AI summary
A method for assessing a camera calibration. In the method, a first quality measure is ascertained, a systematic error having to be assessed using the first quality measure, the assessment being carried out with respect to errors remaining after a calibration, an overall calibration object including at least one calibration object being virtually segmented into calibration objects, a detector noise being estimated for each calibration object, which are combined to form an overall estimate, which is compared with an estimate of the detector noise of the overall calibration object.