Geometric Distortion Compensation in Mobile Image Processing
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Solution Overview
Problem
Existing image processing technologies face challenges in efficiently determining and compensating for geometric distortion in images, particularly in mobile and automated data capture devices, where resources are constrained, and real-time performance is crucial for reliable recognition and data extraction from distorted and noisy imagery.
Innovation Solution
The method employs complementary processes to evaluate a large number of geometric transform candidates, using a fitting process for higher distortion and a correlational process for lower distortion, selecting refined candidates based on detection metrics, and extracting digital payloads using a selected geometric transform, optimized for efficient use of processing resources in mobile devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a large number of geometric transform candidates are evaluated to improve accuracy of distortion recovery, then measurement precision is improved, but processing time and computational resources increase
Solution Approach 1:
The patent segments the evaluation of geometric transform candidates into multiple stages: initial candidate generation, coarse evaluation to filter poor candidates, and refined evaluation of selected candidates. This multi-stage approach maintains high accuracy by thoroughly evaluating promising candidates while reducing total processing time by eliminating poor candidates early in the process.
Solution Approach 2:
The patent applies partial evaluation by not fully processing all geometric transform candidates with the most computationally intensive methods. Instead, it uses lighter initial evaluation methods for all candidates, then applies more rigorous refinement only to candidates that pass initial thresholds, achieving high accuracy for the best candidates without the computational cost of exhaustive full evaluation.
2Reliability
If comprehensive geometric transform evaluation is performed to improve reliability of recognition results, then reliability is improved, but device complexity increases
Solution Approach 1:
The patent divides the complex recognition system into modular components: geometric transform candidate generation module, initial evaluation module, refinement module, and selection module. Each module performs a specific function with well-defined inputs and outputs, making the overall complex system manageable and maintainable while achieving high reliability through comprehensive evaluation.
Solution Approach 2:
The patent implements feedback mechanisms where evaluation results from initial screening inform the refinement process, and detection metrics from refinement feed back into candidate selection. This feedback loop ensures that only promising candidates undergo intensive processing, maintaining high reliability while controlling complexity through intelligent resource allocation.
3Measurement precision
If multiple complementary processes are used to evaluate geometric transform candidates across different distortion ranges, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent segments the geometric transform evaluation into specialized processes for different distortion ranges: one process optimized for low distortion scenarios and another for high distortion scenarios. Each process uses detection metrics and processing strategies tailored to its specific distortion range, improving overall precision by matching the right tool to the right problem while keeping individual process complexity manageable.
Solution Approach 2:
The patent applies local quality by using different evaluation strategies and detection metrics for different ranges of geometric distortion. Rather than using a single complex algorithm for all cases, it employs simpler, optimized processes for specific distortion ranges, reducing overall system complexity while maintaining high precision across the full range of possible distortions.
4Productivity
If real-time processing is implemented to reduce latency, then productivity is improved, but measurement precision may deteriorate
Solution Approach 1:
The patent segments processing into rapid initial candidate generation and evaluation, followed by conditional refinement. The initial segment produces results quickly for real-time responsiveness, while the refinement segment improves precision for candidates that warrant additional processing time based on detection metrics and resource availability.
Solution Approach 2:
The patent implements dynamic processing where the level of refinement applied to each candidate depends on real-time factors such as detection metric thresholds, available processing resources, and time constraints. This dynamic approach allows the system to maintain real-time productivity by adjusting processing depth based on current conditions while preserving the option to achieve higher precision when resources permit.
Data Source
AI summary
An image processing method determines a geometric transform of a suspect image by efficiently evaluating a large number of geometric transform candidates in environments with limited processing resources. Processing resources are conserved by using complementary methods for determining a geometric transform of an embedded signal. One method excels at higher geometric distortion, and specifically, distortion caused by greater tilt angle of a camera. Another method excels at lower geometric distortion, for weaker signals. Together, the methods provide a more reliable detector of an embedded data signal in image across a larger range of distortion while making efficient use of limited processing resources in mobile devices.