Barcode Decoding via Scenario Windowing
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Solution Overview
Problem
Conventional barcode readers are inefficient in decoding multiple barcodes within an image, as they typically only decode the first barcode detected and require time-consuming processing, which is inadequate for applications needing fast response times, especially when images contain additional complex information.
Innovation Solution
A system that processes images by defining regions of interest using scenarios and windows, allowing for the efficient decoding of multiple barcodes by dividing the image into uniform patterns and using pre-configured scenarios to expedite the decoding process, which can be adapted on demand.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional barcode decoding algorithms are used to process the entire image, then all barcodes can be located and decoded, but the processing time is excessive and response speed is slow
Solution Approach 1:
The patent divides the image into multiple regions of interest (ROIs) based on predefined scenarios and window patterns. Instead of processing the entire image, the system segments it into specific areas where barcodes are expected to be located, thereby reducing the search space and processing time while maintaining the ability to detect all relevant barcodes.
Solution Approach 2:
The patent employs pre-configured scenarios and window patterns that are established before image processing. These predefined structures guide the decoding algorithm to focus on specific regions, eliminating the need for exhaustive image scanning and enabling faster barcode location and decoding.
2Measurement precision
If the decoding algorithm is repeated multiple times to locate all barcodes, then more barcodes can be decoded, but the same barcode may be decoded repeatedly and processing efficiency decreases
Solution Approach 1:
By dividing the image into distinct regions of interest using predefined scenarios and windows, the system ensures that each barcode is searched for only once in its specific region. This segmentation prevents redundant decoding of the same barcode while maintaining comprehensive coverage of all barcodes in the image.
Solution Approach 2:
Pre-configured scenarios and window patterns are established before processing, creating a structured search framework. This preliminary organization guides the decoding algorithm through specific regions in a predetermined sequence, ensuring each barcode is decoded exactly once and eliminating repetitive processing.
3Loss of information
If the entire image is processed to locate multiple barcodes, then all encoded information can be extracted, but the processing complexity increases and response time decreases
Solution Approach 1:
The patent segments the image processing task into manageable regions defined by scenarios and windows. Each region is processed independently with simplified algorithms, reducing overall processing complexity while ensuring complete extraction of encoded information from all barcodes through systematic coverage of all regions.
4Reliability
If conventional decoding processes the whole image, then no barcodes are missed, but the response time is too slow for fast response applications
Solution Approach 1:
The system maintains reliable barcode detection by segmenting the image into comprehensive regions of interest that cover all areas where barcodes may be located. Each segment is processed with the full decoding algorithm, ensuring detection reliability is preserved while the overall response speed is improved through reduced processing scope.
Solution Approach 2:
Pre-configured scenarios and window patterns are established before processing to define the search regions. This preliminary action enables the system to quickly focus on relevant areas, maintaining reliable detection of all barcodes while achieving faster response times by avoiding processing of irrelevant image areas.
Data Source
AI summary
A process system for expediting the interpretation of information encoded or contained with an optical image capture for barcode decoding and machine vision applications that uses one or more each of which define one or more windows arranged according to equal rows or columns in the captured image that will be further processed to extract encoded or designer information. The scenarios and windows contained therein may be defined in memory and then recalled as needed based on the particular application so that multiple barcodes contained within a single image may be efficiently and expeditiously decoded.