Barcode Scanner Decoding Sub-sampled Contrast Image
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Solution Overview
Problem
Two-dimensional bar codes, such as DataMatrix symbols, face challenges in direct part marking due to irregular surfaces and varying reflectivity, making it difficult to accurately decode symbols imprinted on non-flat or metallic parts with circular dots instead of square modules.
Innovation Solution
A method is employed to decode two-dimensional symbols from gray scale images by forming a sub-sampled image based on local contrast levels, determining symbol margins, and using a threshold surface to binarize the image, allowing for efficient detection of symbol outlines and module states even on irregular surfaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional binarization methods are used on gray scale images of bar codes imprinted on irregular surfaces, then the decoding process becomes computationally intensive and time-consuming, but the accuracy of symbol margin detection deteriorates due to varying reflectivity and surface irregularities
Solution Approach 1:
The patent divides the gray scale image processing into distinct segments: first creating a binary image to detect symbol margins and outline, then using that outline to guide selective processing of the original gray scale image for digitization. This segmentation allows efficient margin detection without requiring intensive processing of the entire high-resolution image, resolving the contradiction between accuracy and time.
Solution Approach 2:
The patent performs preliminary binarization and outline detection before final digitization. By first creating a binary representation to identify symbol boundaries and margins, the system establishes a framework that guides subsequent processing, avoiding the need for computationally intensive analysis of the complete gray scale image and reducing overall decoding time while maintaining accuracy.
2Adaptability or versatility
If bar code symbols are imprinted directly on parts with irregular surfaces or varying reflectivity properties, then the compact size advantage of DataMatrix symbols is achieved for direct part marking, but the reliability of decoding deteriorates due to contrast variation and surface irregularities
Solution Approach 1:
The patent applies local quality by using the detected symbol outline to guide selective processing. The binary image provides local information about symbol boundaries and margins, which then directs the digitization process to focus computational resources on relevant regions. This local approach maintains decoding reliability by ensuring accurate capture of symbol features while adapting to variations in surface properties and reflectivity.
Solution Approach 2:
The patent introduces a binary image as an intermediary between the original gray scale image and the final decoded data. This intermediate representation captures the essential structural information (margins, outline, module positions) while filtering out noise from surface irregularities and reflectivity variations. The intermediary binary image serves as a robust bridge that maintains decoding reliability despite challenging imaging conditions on irregular surfaces.
3Measurement precision
If the entire gray scale image is processed for binarization to handle contrast variation, then decoding accuracy can be maintained, but the device complexity and computational requirements increase significantly
Solution Approach 1:
The patent segments the processing task into two phases: first, binary image processing to detect symbol margins and outline with simple thresholding; second, selective digitization of the original gray scale image using the outline as a guide. This segmentation reduces device complexity by avoiding the need for complex processing of the entire high-resolution image, while maintaining module state detection accuracy through targeted analysis of relevant regions.
Solution Approach 2:
The patent applies partial action by processing only the portions of the image that are necessary for decoding. The binary outline detection identifies the symbol boundaries and internal structure, allowing the system to focus subsequent digitization efforts on these identified regions rather than processing the entire image at full resolution. This partial processing approach reduces computational complexity while maintaining sufficient accuracy for reliable decoding.
Data Source
AI summary
A two dimensional symbol is decoded by forming a sub-sampled image that is used to generate symbol margins for use in digitization. The sub-sampled image is based on local levels of contrast in the symbol. By using a sub-sampled image derived from local levels of contrast in the symbol to determine the symbol margins, margins can be located efficiently and in the presence of symbol contrast variation caused by irregularities in the marked surface or light source angles. Digitization proceeds based on the determined margins.