2D Matrix Symbol Decoding via Salient Feature Detection
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Solution Overview
Problem
Existing imaging systems struggle to effectively read two-dimensional matrix symbols with damaged or absent fixed patterns, as they rely on locating these patterns for decoding, which fails when they are incomplete or missing.
Innovation Solution
A system and method that utilize a processor and image acquisition software to locate candidate regions, prioritize them, and estimate the extent of the symbol, even without a complete fixed pattern, by identifying salient features and using subpixel interpolation to determine module positions and values, allowing for decoding of incomplete or pattern-less two-dimensional matrix symbols.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system relies on locating fixed patterns for decoding, then decoding accuracy is improved, but the system fails when fixed patterns are damaged or absent
Solution Approach 1:
The patent segments the symbol decoding process into multiple independent approaches: (1) traditional fixed pattern location, (2) corner detection method, and (3) salient feature identification. By dividing the decoding task into multiple pathways, the system can switch between methods depending on symbol condition, maintaining reliability while preserving accuracy through the primary fixed pattern method when available.
Solution Approach 2:
The system dynamically changes operational parameters based on symbol integrity. When fixed patterns are detected as damaged or absent, the system transitions from fixed pattern-based decoding to alternative methods including corner detection and salient feature identification. This parameter change allows the system to adapt to varying symbol conditions, maintaining reliability without permanently sacrificing the precision of fixed pattern matching.
2Productivity
If the system uses traditional fixed pattern location, then decoding speed is improved, but the system cannot handle damaged or absent fixed patterns
Solution Approach 1:
The patent implements a dynamic decoding system that automatically selects the appropriate method based on real-time analysis of symbol integrity. The system begins with fast fixed pattern location for speed, but dynamically switches to alternative methods when damage is detected. This dynamic adaptation maintains high productivity for intact symbols while providing versatility for damaged or atypical symbols.
Solution Approach 2:
The system incorporates multiple decoding functions within a single unified framework. It can perform fixed pattern location, corner detection, and salient feature identification, making it universally applicable to various symbol conditions. This multi-functionality allows the same system to handle both intact symbols (maintaining speed) and damaged/absent pattern symbols (gaining adaptability).
3Measurement precision
If the system identifies fixed points in symbols, then decoding precision is improved, but the method becomes useless when fixed points cannot be located
Solution Approach 1:
The patent introduces salient features as intermediary elements between fixed patterns and data regions. When fixed patterns are damaged, these intermediaries (corners, edges, distinctive geometric features) serve as alternative reference points. The system uses these intermediaries to establish coordinate systems and locate data regions, maintaining precision without relying solely on fixed patterns, thereby improving damage tolerance.
Solution Approach 2:
The system prepares multiple decoding strategies in advance to cushion against fixed pattern damage. By pre-programming alternative methods (corner detection, salient feature identification) alongside fixed pattern location, the system ensures that if fixed points become unavailable due to damage,备用 methods are already in place to maintain decoding precision without requiring real-time method development.
Data Source
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AI summary
Systems and methods for reading a two-dimensional matrix symbol or for determining if a two-dimensional matrix symbol is decodable are disclosed. The systems and methods can include a data reading algorithm that receives an image, locates one or more candidate regions in the image, prioritizes the one or more candidate regions based on an edge density of the one or more candidate regions, and extracts a binary matrix from the one or more candidate regions.