Visual Encoding Recognition With Multi-System Context Verification
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Solution Overview
Problem
Existing visual encoding systems face challenges in accurately interpreting patterns at varying distances and are susceptible to fraudulent activities due to the inability to verify contextual information, leading to inefficiencies and security vulnerabilities.
Innovation Solution
A computing system employs multiple recognition systems operating on different data signals and processing techniques to recognize visual encodings, leveraging contextual information such as GPS data, ambient noise, and surrounding visual features to enhance accuracy and security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single recognition system is used to process visual encodings, then the processing speed is maintained, but the accuracy and security against fraudulent activities deteriorate
Solution Approach 1:
The patent divides the recognition system into multiple independent recognition systems, each specialized in detecting different aspects of visual encodings (e.g., barcodes, QR codes, data matrices). Each system processes the same image data independently, and their results are combined to achieve higher overall accuracy and security without requiring a single complex system.
2Reliability
If multiple recognition systems are deployed to verify contextual information, then security and fraud detection improve, but processing time and computational resources increase
Solution Approach 1:
The patent implements a tiered verification approach where not all recognition systems must achieve complete verification for basic processing to occur. Some systems perform rapid partial verification while others conduct more thorough analysis, allowing the system to balance security requirements with processing efficiency by accepting partial verification results in certain contexts.
3Measurement precision
If high-resolution imaging is used to discern visual encoding features, then pattern recognition accuracy improves, but the ability to capture encodings at varying distances deteriorates
Solution Approach 1:
The patent employs multiple recognition systems that are each optimized for different encoding types and detection scenarios. This universal approach allows the overall system to handle various distances and encoding formats effectively, as different systems can be selectively activated based on the specific encoding being processed, rather than requiring a single high-resolution system that may struggle with distance variations.
Data Source
AI summary
The present disclosure relates generally to the processing of machine-readable visual encodings in view of contextual information. One embodiment of aspects of the present disclosure comprises obtaining image data descriptive of a scene that includes a machine-readable visual encoding; processing the image data with a first recognition system configured to recognize the machine-readable visual encoding; processing the image data with a second, different recognition system configured to recognize a surrounding portion of the scene that surrounds the machine-readable visual encoding; identifying a stored reference associated with the machine-readable visual encoding based at least in part on one or more first outputs generated by the first recognition system based on the image data and based at least in part on one or more second outputs generated by the second recognition system based on the image data; and performing one or more actions responsive to identification of the stored reference.