Stereo Misalignment Estimation Using Modified Affine Models
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Solution Overview
Problem
Current methods for stereo camera systems rely on expensive hardware calibration to address misalignment issues, which is often unavailable or impractical.
Innovation Solution
A method using modified affine or perspective models to estimate stereo misalignment by dividing frames into blocks, comparing boundary signals, estimating motion vectors, and applying these to a transformation model with a temporal filter to adjust frames without hardware calibration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If hardware calibration is used to resolve stereo misalignment, then alignment accuracy is improved, but cost and availability deteriorate
Solution Approach 1:
The patent replaces hardware calibration mechanisms with a software-based computational approach. Instead of using physical calibration tools and hardware adjustments, the system uses image processing algorithms that analyze boundary signals and apply affine or perspective transformation models to estimate and correct stereo misalignment, thereby eliminating the need for expensive hardware calibration equipment
Solution Approach 2:
The patent creates a virtual calibration model through software that replicates the function of hardware calibration. By computing transformation parameters from image data and applying them digitally to correct misalignment, the system produces the same alignment effect as hardware calibration would, but through a software copy of the calibration process rather than physical hardware
2Ease of manufacture
If software calibration is used to estimate stereo misalignment, then cost and availability are improved, but computational complexity increases
Solution Approach 1:
The patent divides the image into blocks and processes boundary signals from each block separately to estimate motion vectors. This segmentation allows the computational task to be distributed and managed in smaller units, reducing the overall computational complexity compared to processing the entire image as a single unit
Solution Approach 2:
The patent applies different processing strategies to different parts of the image based on local characteristics. By focusing computation on boundary signals and selecting reliable motion vectors from local regions, the system reduces unnecessary computational operations while maintaining accuracy in critical areas
3Measurement precision
If blocks are divided into smaller features, then estimation accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent applies transformation models to selected blocks rather than all blocks in the image. By choosing representative blocks and applying the affine or perspective models only to these partial regions, the system achieves sufficient estimation accuracy without the excessive computational cost of processing every possible block
Data Source
AI summary
A method and apparatus for estimating stereo misalignment using modified affine or perspective model. The method includes dividing a left frame and a right frame into blocks, comparing horizontal and vertical boundary signals in the left frame and the right frame, estimating the horizontal and the vertical motion vector for each block in a reference frame, selecting a reliable motion vectors from a set of motion vectors, dividing the selected block into smaller features, feeding the data to an affine or a perspective transformation model to solve for the model parameters, running the model parameters through a temporal filter, portioning the estimated misalignment parameters between the left frame and right frame, and modifying the left frame and the right frame to save some boundary space.