Automated Visual Sensor Calibration via Digital Interest Points
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current calibration methods for spatial relationships between visual sensors and medical or imaging devices are costly and involve complex, manual procedures, necessitating a more efficient and automated approach.
Innovation Solution
A system and method utilizing a processor and storage device to automatically calibrate by obtaining interest points from prior information, constructing a feature library, and determining transformation relationships between coordinate systems based on image data, reducing the need for calibration apparatuses and markers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional calibration apparatuses and markers are used for spatial relationship calibration, then calibration accuracy can be maintained, but manufacturing and maintenance costs increase significantly
Solution Approach 1:
The patent replaces physical calibration apparatuses and markers with their digital representations (images). The system captures images of the device and uses image processing to extract feature points, creating a digital copy of the calibration targets. This eliminates the need for manufacturing and maintaining expensive physical calibration equipment while preserving the spatial relationship information needed for accurate calibration.
Solution Approach 2:
The device being calibrated serves its own calibration function by providing visual features that can be detected by the camera. The device's own structure, markings, or components become the calibration targets, eliminating the need for separate external calibration apparatuses. The system uses the device itself as the calibration reference, reducing external dependencies and costs.
2Measurement precision
If manual calibration procedures are used with calibration apparatuses, then spatial relationship can be established, but the calibration process becomes complex and time-consuming
Solution Approach 1:
The patent replaces manual mechanical calibration procedures with an automated optical and computational system. Instead of manual manipulation of calibration apparatuses and measurements, the system uses a camera to capture images, automated image processing to extract feature points, and computational algorithms to determine the transformation matrix. This substitution of mechanical manual processes with optical and computational methods simplifies the procedure while maintaining accuracy.
Solution Approach 2:
The patent introduces image data as an intermediary between the physical device and the calibration computation. Rather than directly measuring physical dimensions and positions manually, the system captures images of the device, extracts feature points from these images, and uses these intermediate digital representations to compute the spatial transformation. This intermediary step automates the process and reduces manual complexity.
3Productivity
If automated image-based calibration is implemented, then calibration time and complexity are reduced, but the need for accurate feature point identification increases
Solution Approach 1:
The patent applies preliminary processing to the images before feature point extraction, including image enhancement, filtering, and preprocessing steps that improve the quality and detectability of features. By preparing the images in advance with appropriate processing, the system makes feature points easier to identify accurately, reducing the difficulty of detection while maintaining high calibration speed through automation.
Data Source
AI summary
A method for automated calibration is provided. The method may include obtaining a plurality of interest points based on prior information regarding a device and image data of the device captured by a visual sensor. The method may include identifying at least a portion of the plurality of interest points from the image data of the device. The method may also include determining a transformation relationship between a first coordinate system and a second coordinate system based on information of at least a portion of the identified interest points in the first coordinate system and in the second coordinate system that is applied to the visual sensor or the image data of the device.