Geographic Image Alignment via Multi-Resolution Feature Matching
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Solution Overview
Problem
Existing methods for aligning geographic images based on geo-location data are laborious, error-prone, and impractical for large numbers of assets due to inaccuracies and errors in image positioning and photogrammetric distortions, requiring manual intervention and skilled operators.
Innovation Solution
A system and method that processes multiple images by identifying matching features across different resolutions, calculating alignment vectors, and adjusting geo-location data to align images accurately, using a pyramid structure of tiles and feature matching algorithms to automate the alignment process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual stitching methods are used to align images, then alignment accuracy can be improved, but labor time and operational complexity increase significantly
Solution Approach 1:
The system performs self-alignment by automatically detecting features, computing transformations, and adjusting geo-location data without human intervention. The computer executes algorithms that autonomously identify matching features across images and calculate the necessary alignment transformations, eliminating the need for manual operator input while maintaining high accuracy.
Solution Approach 2:
The patent replaces the manual mechanical process of visual inspection and point-by-point matching with an automated computational system. Computer vision algorithms and mathematical transformation models substitute for human operators, enabling rapid processing of multiple images through algorithmic feature detection and geometric transformation calculations.
2Measurement precision
If manual stitching methods are used to align images, then alignment accuracy can be improved, but operational complexity increases due to requiring skilled operators
Solution Approach 1:
The system performs self-alignment by automatically detecting features, computing transformations, and adjusting geo-location data without human intervention. The computer executes algorithms that autonomously identify matching features across images and calculate the necessary alignment transformations, eliminating the need for manual operator input while maintaining high accuracy.
3Measurement precision
If traditional alignment methods are used, then accuracy can be maintained, but scalability to large datasets is poor
Solution Approach 1:
The patent segments the alignment process into distinct computational stages: feature extraction, feature matching, transformation computation, and geo-location adjustment. This modular approach allows each stage to be optimized independently and enables parallel processing of multiple images, significantly improving scalability to large datasets while maintaining accuracy through systematic processing of each segment.
Solution Approach 2:
The system performs preliminary feature extraction and matching across all images before computing final transformations. By pre-identifying matching features and establishing correspondence relationships in advance, the system prepares the data structure needed for rapid batch processing, enabling efficient scaling to large numbers of images without sacrificing alignment precision.
4Measurement precision
If feature matching across multiple resolutions is performed, then alignment precision is improved, but computational requirements increase
Solution Approach 1:
The patent segments the alignment process into distinct computational stages: feature extraction, feature matching, transformation computation, and geo-location adjustment. This modular approach allows each stage to be optimized independently and enables parallel processing of multiple images, significantly improving scalability to large datasets while maintaining accuracy through systematic processing of each segment.
Data Source
AI summary
A system and method is provided for aligning images of one resolution based on both features contained in the images and the alignment of related images of a different resolution.