Dual-View Image Calibration via Fundamental Matrix Optimization
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Solution Overview
Problem
Current dual-view angle image calibration methods face challenges in accurately aligning images taken from different angles, leading to calibration errors due to lens displacement and requiring complex setup and specialized personnel, which complicates the production of dual-camera devices.
Innovation Solution
A dual-view angle image calibration method that performs feature matching on image pairs, calculates multiple fundamental matrices, determines an optimization fundamental matrix based on deformation information, and calibrates the images to achieve automatic alignment, reducing calibration errors and simplifying the production process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional dual-view angle image calibration methods are used, then calibration can be performed, but calibration errors occur due to lens displacement and complex setup is required
Solution Approach 1:
The system performs self-calibration by automatically detecting feature points in the image pair and computing the fundamental matrix without requiring external calibration equipment or manual intervention. The calibration process is embedded within the image processing pipeline itself, allowing the dual-camera system to calibrate itself during normal operation.
Solution Approach 2:
The patent replaces mechanical calibration setups (physical calibration targets, manual alignment tools) with computational methods. By using feature point detection and fundamental matrix computation, the system substitutes physical calibration mechanisms with algorithmic processing, thereby eliminating the need for complex mechanical setups and specialized personnel.
2Measurement precision
If traditional calibration methods are used, then calibration can be performed, but specialized personnel are required which complicates production
Solution Approach 1:
The calibration system is fully automated and self-executing, requiring no trained personnel to operate calibration equipment. The algorithm automatically processes images, detects features, and computes calibration parameters, making the production process accessible to standard operational personnel without specialized training.
Solution Approach 2:
The calibration data and fundamental matrices are pre-computed and stored during manufacturing or initial setup. These pre-computed calibration parameters are then reused during normal operation, eliminating the need for repeated manual calibration processes and reducing production complexity.
3Measurement precision
If multiple fundamental matrices are calculated, then more accurate calibration is achieved, but computational complexity increases
Solution Approach 1:
The system computes multiple fundamental matrices from different feature point subsets to ensure accuracy, but applies pruning strategies to eliminate redundant computations. By calculating only the necessary number of matrices and using efficient selection criteria, the system achieves high calibration accuracy without excessive computational overhead.
Solution Approach 2:
The computational process is divided into distinct stages: feature point detection, fundamental matrix computation from subsets, validation of multiple matrices, and selection of the optimal matrix. This segmentation allows the system to manage computational complexity by processing tasks in manageable steps rather than as a monolithic computation.
Data Source
AI summary
A dual-view angle image calibration method includes: performing feature matching on a first image pair to obtain a first feature point pair set, the first image pair including two images respectively photographed corresponding to two different view angles of a same scene; obtaining multiple different first fundamental matrices of the first image pair at least according to the first feature point pair set, and obtaining first image deformation information indicating relative deformation between the first image pair after mapping transformation is performed through the first fundamental matrices and the first image pair before the mapping transformation is performed; determining a first optimization fundamental matrix from the multiple first fundamental matrices at least according to the first image deformation information; and calibrating the first image pair according to the first optimization fundamental matrix.