Imaging Device Calibration via Quadric Surface Segmentation
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Solution Overview
Problem
Traditional camera calibration methods for mixed reality environments are cumbersome, requiring specific visual patterns that can be damaged or misplaced, leading to erroneous experiences and necessitating user training, and often need to be repeated for each meeting session or when the camera position changes.
Innovation Solution
An automated calibration system that generates a reference coordinate frame aligned with a meeting room table, using off-the-shelf RGB cameras, and performs partial view table calibration without requiring specific visual patterns, allowing for the insertion of virtual 4D content and eliminating the need for head-mounted devices or complex calibration techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional camera calibration methods using specific visual patterns are employed, then calibration accuracy can be achieved, but the system becomes complex and requires user training
Solution Approach 1:
The calibration system automatically detects and processes visual patterns without requiring user intervention or training. The system self-calibrates by autonomously identifying fiducial markers, computing transformation matrices, and adjusting camera parameters, thereby eliminating the need for user-trained operators while maintaining high calibration accuracy
Solution Approach 2:
The patent introduces an automated image processing pipeline as an intermediary between the visual patterns and calibration results. This intermediary system uses computer vision algorithms to automatically detect fiducial markers, compute their coordinates, and generate calibration data, thereby reducing the complexity of direct user interaction with the calibration process
2Measurement precision
If specific visual patterns are used for calibration, then accurate calibration can be performed, but the patterns can be damaged or misplaced leading to errors
Solution Approach 1:
The system performs preliminary detection and validation of visual patterns before proceeding with calibration. It pre-identifies fiducial markers, verifies their integrity and positioning, and computes preliminary transformation matrices. This preliminary action ensures that only valid, undamaged patterns are used for calibration, thereby maintaining both accuracy and reliability
Solution Approach 2:
The calibration system incorporates feedback mechanisms that continuously monitor the quality and position of visual patterns. If patterns are detected as damaged, misplaced, or insufficient, the system provides feedback to reject those patterns and automatically selects alternative valid patterns, thereby ensuring calibration reliability without compromising accuracy
3Ease of manufacture
If traditional calibration procedures are implemented, then calibration can be completed, but it requires user training and time
Solution Approach 1:
The calibration system performs all calibration operations autonomously without requiring user training or manual intervention. It automatically captures images, detects visual patterns, computes calibration parameters, and applies corrections, thereby eliminating the time-consuming aspect of user training while maintaining ease of use through full automation
Solution Approach 2:
The system performs preliminary automated setup and configuration before the actual calibration process. It pre-configures camera parameters, automatically identifies the calibration scene, and prepares the processing pipeline in advance. This preliminary action reduces the overall calibration time by eliminating setup delays and user configuration steps
4Reliability
If calibration is performed for each meeting session or camera position change, then accurate mixed reality experience is maintained, but productivity decreases
Solution Approach 1:
The calibration system dynamically adapts to different meeting sessions and camera positions by automatically detecting whether recalibration is necessary. It uses dynamic thresholding and change detection algorithms to determine if the camera has moved or if environmental conditions have changed, recalibrating only when necessary. This dynamic approach maintains mixed reality quality while minimizing unnecessary recalibration that would reduce productivity
Data Source
AI summary
Systems, apparatus, articles of manufacture, and methods are disclosed to calibrate imaging devices. An example apparatus includes interface circuitry, machine readable instructions, and programmable circuitry to at least one of instantiate or execute the machine readable instructions to segregate an image into regions. The example apparatus binarizes the image by associating a first one of the regions with a surface, and associating a second one of the regions with background objects. The example apparatus also generates a quadric corresponding to the surface, distinguishes a first quantity of pixels from a second quantity of pixels from the binarized image, the first quantity of pixels associated with the first one of the regions and the second quantity of pixels associated with the second one of the regions, adjusts parameters of the quadric based on the first quantity of the pixels, and calculates calibration parameters based on the adjusted parameters of the quadric.