Infrared Camera Geometry Calibration for Multitouch Displays
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Solution Overview
Problem
Camera-based multitouch displays face challenges in optimal geometry calibration due to variations in mechanical tolerances, leading to inaccuracies in recognizing touch inputs and determining object boundaries, as cameras are not always placed exactly as designed, affecting the quality of infrared-based multitouch recognition.
Innovation Solution
An automatic geometry calibration method for infrared-based multitouch displays, which adjusts camera region of interest (ROI), optimizes lens distortion parameters, and corrects image alignment using digital enhancement and error function optimization, allowing for precise calibration without the need for external hardware or precise alignment of calibration sheets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual calibration is performed for each camera to account for mechanical tolerances, then measurement precision improves, but device complexity and time consumption increase
Solution Approach 1:
The system performs automatic self-calibration by capturing images of the touch screen bezel and autonomously computing geometric correction parameters without requiring external calibration tools or manual intervention. The control unit automatically processes the captured images to determine camera position, orientation, and distortion parameters.
Solution Approach 2:
The system uses the touch screen bezel itself as a calibration reference, capturing its geometric features through the camera and using these captured features to compute correction parameters. This eliminates the need for separate external calibration artifacts or templates.
2Measurement precision
If separate calibration is performed for each camera, then measurement precision improves, but productivity decreases
Solution Approach 1:
The system combines multiple camera calibration operations into a single automated process. By capturing images from all cameras simultaneously and processing them through a unified computational algorithm, the system achieves what would otherwise require multiple separate manual calibration procedures.
Solution Approach 2:
The system automatically adjusts camera parameters including position, orientation, and distortion coefficients based on computational analysis of captured bezel images. This automated parameter optimization replaces time-consuming manual adjustment while maintaining high precision.
3Measurement precision
If camera ROI and distortion parameters are optimized per camera, then measurement precision improves, but ease of operation worsens
Solution Approach 1:
The control unit autonomously performs the complete calibration process including capturing images, computing geometric parameters, determining distortion coefficients, and optimizing ROI settings. This eliminates the need for operators to manually adjust multiple camera parameters while achieving high precision results.
Solution Approach 2:
The system replaces manual mechanical adjustment of camera parameters with automated computational methods. Digital image processing and algorithmic parameter optimization substitute for physical camera adjustment, simplifying the operator's task while improving precision.
4Measurement precision
If multiple calibration parameters are adjusted per camera, then measurement precision improves, but device complexity increases
Solution Approach 1:
The control unit automatically computes and adjusts all calibration parameters including camera position, orientation, distortion coefficients, and ROI settings without external intervention. This self-calibration capability handles the complexity of multiple parameters internally while presenting a simple interface to users.
Solution Approach 2:
The system uses the touch screen bezel's geometric features as a reference model for calibration. By capturing and analyzing the bezel's known geometry through each camera, the system computes all necessary correction parameters systematically, managing complexity through a unified reference framework.
Data Source
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AI summary
A touch display has a touch screen to display images and one or more infrared cameras to take images of objects contacting the touch screen. One or more calibration patterns are formed on the touch screen. In response, an automatic process takes place, comprising: producing an image of the touch screen by taking with each one of the group of infrared cameras a set of one or more images of the calibration patterns; determining geometric distortions of the images with relation to the formed one or more calibration patterns; and calculating calibration parameters configured to enable computational compensation of the determined geometric distortions. The calibration parameters are then stored.