Image Rectification via Parallel Motion Vector Analysis
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Solution Overview
Problem
Conventional image calibration methods are limited in accuracy and require complex assumptions or multiple vehicle passages, making them inefficient for precise geometric calibration and rectification, especially when dealing with straight roadways or objects with parallel motion vectors.
Innovation Solution
A method that uses two images to calculate motion vectors and assumes parallelism for projective, affine, and isometric rectification, allowing for precise calibration and rectification with minimal input parameters, enabling high accuracy and reduced computational effort.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional calibration methods using multiple marking points and statistical evaluation are used, then calibration can be performed, but measurement precision and accuracy are limited
Solution Approach 1:
The invention extracts and utilizes only the essential geometric features (parallel lines and motion vectors) from the calibration object, eliminating the need for multiple marking points and complex statistical evaluations. By focusing on the critical parallelism relationship between lines and motion vectors, the method achieves high precision with simplified input requirements.
Solution Approach 2:
The invention changes the calibration approach from using multiple scattered marking points to using geometric parameters (parallel lines and motion vectors) that inherently encode spatial relationships. This parameter transformation enables direct calculation of calibration coefficients through geometric constraints, improving both precision and efficiency.
2Measurement precision
If methods requiring multiple vehicle passages or object movements are used, then calibration data can be collected, but time consumption increases
Solution Approach 1:
The invention performs preliminary geometric analysis by identifying parallel lines and motion vectors in the image sequence before proceeding to calibration calculations. By pre-processing the data to extract these geometric features, the method eliminates the need for multiple repeated passages and achieves reliable calibration from minimal input data.
Solution Approach 2:
The invention uses the geometric relationships (parallelism of lines and motion vectors) as a simplified copy or representation of the full calibration problem. Instead of requiring complete multi-pass数据采集, the parallel geometric structures serve as sufficient proxies that enable accurate calibration with significantly reduced time requirements.
3Measurement precision
If complex calibration procedures with multiple assumptions are used, then calibration can be performed, but ease of operation decreases
Solution Approach 1:
The calibration object itself provides the necessary calibration information through its inherent geometric properties (parallel lines and motion vectors). The method is self-sufficient, requiring no external calibration patterns or multiple marking points, as the object's own geometric structure serves as the calibration reference, greatly simplifying operation.
Solution Approach 2:
The invention transforms the calibration problem into a geometric parameter estimation task, where only parallel lines and motion vectors need to be identified. This parameter simplification reduces the operational complexity from managing multiple marking points and statistical models to simply detecting geometric features, making the method easier to implement while maintaining high accuracy.
Data Source
Figure 1~2
AI summary
Method for calibration using at least one first image acquisition and one second image acquisition, wherein a first point P1 and a second point P2 are moved along a motion vector L1 and L2 respectively relative to a viewing point with a velocity v1 and v2 respectively, a first image acquisition shows a distorted point P1'(t1) and a distorted point P2'(t1) at a time t1 and a second image acquisition shows a distorted point P1'(t2) and a distorted point P2'(t2) at a time t2, wherein a first motion vector L1' describing the movement of the first distorted point P1' from the position P1'(t1) to the position P1'(t2) from the image acquisitions and a second motion vector L2' describing the movement of the second distorted point P2' from the position P2'(t1) to the position P2'(t2) from the image acquisitions is measured, such that a mathematical modeling is feasible.