Camera Parameter Self-Calibration via Iterative Feature Concordance
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Solution Overview
Problem
Existing methods for determining camera parameters during self-calibration are costly and inefficient, particularly when tracking a smartphone camera while recording a surrounding area for augmented reality applications, as they require extensive feature matching and are not suitable for real-time processing.
Innovation Solution
A method that determines camera parameters by comparing sets of visual features extracted from images and feature images, iteratively adjusting parameters to achieve concordance, using a three-dimensional geometrical description of the surrounding area and feature detection, allowing for real-time self-calibration without direct feature correspondence, and can be supported by sensors or metering devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional feature matching methods are used for camera parameter determination, then measurement precision is improved, but computation time and processing cost increase significantly
Solution Approach 1:
The patent extracts only the essential geometric information (point clouds representing visual features) from images, discarding other visual properties such as color, texture, and shape details. This extraction of core geometric data reduces computation time while maintaining sufficient precision for camera parameter determination through iterative optimization.
Solution Approach 2:
The patent transforms the camera parameter determination problem by changing parameters iteratively - adjusting camera position, orientation, and optical properties in successive optimization cycles. This parameter-based approach replaces traditional feature matching with a more efficient optimization process that achieves comparable precision faster.
2Manufacturing precision
If comprehensive feature matching is performed, then manufacturing precision is improved, but device complexity increases
Solution Approach 1:
The patent simplifies the processing system by extracting only point cloud data representing visual feature positions, eliminating the need for complex visual property analysis. This reduces device complexity while maintaining determination accuracy through focused geometric comparison.
Solution Approach 2:
The patent replaces traditional mechanical feature matching systems with an iterative optimization approach that uses mathematical parameter adjustment. This substitution simplifies the overall system architecture by replacing complex matching algorithms with a more straightforward optimization framework.
3Measurement precision
If traditional self-calibration methods are used, then measurement precision is improved, but productivity decreases
Solution Approach 1:
The patent extracts only essential point cloud data for real-time processing, discarding redundant visual information. This extraction enables faster processing suitable for real-time applications while maintaining sufficient precision for camera parameter determination through efficient iterative optimization.
Solution Approach 2:
The patent implements real-time capability by enabling rapid parameter changes through iterative optimization. The system can quickly adjust camera parameters in successive cycles, achieving both precision and speed required for real-time self-calibration during smartphone camera tracking.
Data Source
AI summary
A method and image processing system determine parameters of a camera. According to the method, an image of a surrounding area is captured by the camera, and camera parameters are initially determined. Furthermore, a three-dimensional geometric description of visual features of the surrounding area is provided. A feature detector is used on the captured image in order to extract visual features. The initially determined camera parameters are applied to the three-dimensional geometric description of the visual features of the surrounding area in order to display said visual features on a feature image. A quantity of the visual features extracted from the image is compared with a quantity of the visual features in the feature image to determine a degree of concordance between the two quantities. The camera parameters are changed repeatedly to determine additional feature images for which the degree of concordance is determined until said degree exceeds a threshold.