Light-Invariant Pattern Mapping for Real-Time Image Alignment
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Solution Overview
Problem
Implementing image registration on resource-limited devices, such as smartphones, is challenging due to the computational complexity of techniques like cross correlation and least squares optimization, which are difficult to execute in real time without matrix approximations, leading to computational slowness and low-quality results.
Innovation Solution
A correlation system that implements least squares optimization of normalized cross correlation without approximation, using a Jacobian of the data normalization function, and an iterative update step to improve efficiency and robustness, enabling image alignment on resource-limited devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If cross correlation and least squares optimization are implemented without matrix approximations, then measurement precision and reliability are improved, but computation time increases and productivity decreases
Solution Approach 1:
The patent segments the image registration process into distinct phases: feature detection, feature matching, and transformation calculation. By processing images in stages rather than computing full cross-correlation matrices, the system achieves accurate alignment while reducing computational burden on resource-limited devices.
Solution Approach 2:
The patent applies partial action by using simplified correlation computations for initial feature matching, then refining results with least squares optimization only on selected key points. This selective approach maintains measurement precision for critical alignment parameters while avoiding the computational expense of applying full optimization to all image pixels.
2Productivity
If resource-limited devices attempt real-time image registration, then productivity increases, but measurement precision and reliability deteriorate due to computational constraints
Solution Approach 1:
The system segments computation into hierarchical levels: fast feature detection algorithms run on mobile devices for real-time responsiveness, while more computationally intensive optimization steps are performed selectively or in the cloud. This enables real-time processing with maintained precision through coordinated multi-level computation.
Solution Approach 2:
The patent dynamically adjusts computation parameters based on device capabilities and performance requirements. For resource-constrained devices, it modifies optimization iteration counts, feature point densities, and correlation window sizes to achieve acceptable real-time performance while preserving sufficient alignment accuracy for practical applications.
3Productivity
If matrix approximations are used to speed up computation, then productivity increases, but measurement precision and reliability of image registration deteriorate
Solution Approach 1:
The patent applies matrix approximations partially rather than universally. Full-precision least squares optimization is applied to critical transformation parameters, while approximations are used for preliminary feature matching and less sensitive computations. This selective precision strategy maintains registration reliability where it matters most while achieving computational efficiency through strategic approximation elsewhere in the pipeline.
Data Source
AI summary
A correlation system can be implemented on a user device (e.g., smartphone) to perform image processing tasks e.g., point to point correlations for tasks such as alignment, tracking, and reconstruction of model data. The correlation system can implement normalized cross correlation in a least squares optimization scheme without use of approximations. Normalized cross correlation optimized via least squares can further implement global and local cost functions that are invariant to dynamic lighting conditions.