Indicia Decoder Parameter Optimization via Automated Image Analysis
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Solution Overview
Problem
Machine vision systems face inefficiencies due to the manual and time-consuming process of configuring decoder parameters for indicia decoders, leading to suboptimal decode speed and reliability, as users rely on guesswork to find suitable configurations without ensuring the best possible settings are tested.
Innovation Solution
A method that automatically adjusts decoder parameters by applying a decoder algorithm to multiple image data sets to determine optimal settings, comparing decode results to set parameters, and iteratively refining these settings for improved performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual parameter configuration is used, then users can adjust decoder parameters, but the process is time-consuming and yields suboptimal settings
Solution Approach 1:
The system performs self-configuration by automatically analyzing image data sets and adjusting decoder parameters without user intervention. The indicia decoder captures image data, applies the decoder algorithm with adjusted parameters, and autonomously determines optimal settings based on decode performance, eliminating the need for manual parameter tuning while reducing configuration time
Solution Approach 2:
The system performs preliminary parameter optimization by analyzing multiple image data sets before actual decoding operations. The decoder adjusts parameters based on preliminary analysis of image characteristics and decode results, establishing optimal settings in advance to avoid time-consuming manual configuration during operation
2Ease of operation
If manual parameter tuning is used, then some configuration can be achieved, but decode accuracy and reliability are compromised
Solution Approach 1:
The system implements feedback mechanisms where decode results from image data sets are analyzed to determine whether parameter adjustments improved performance. The decoder counts decoded indicia with new parameters and compares against previous results, using this feedback to iteratively refine parameters and ensure decode reliability is maintained or improved
Solution Approach 2:
The decoder parameters are made dynamic and adjustable based on actual image data characteristics rather than being static. The system automatically modifies parameters such as contrast threshold, module size, and quiet zone size according to the specific image being processed, enabling adaptive optimization that improves decode reliability across varying conditions
3Reliability
If extensive parameter combinations are tested, then optimal settings may be found, but the process becomes impractically time-consuming
Solution Approach 1:
The system efficiently explores parameter space by making targeted parameter changes based on image data analysis rather than exhaustively testing all combinations. The decoder adjusts specific parameters (contrast threshold, module size, quiet zone size) based on characteristics detected in the image data, achieving optimal decode accuracy without requiring extensive testing of every possible parameter combination
Data Source
AI summary
Methods and systems for optimizing one or more decoder parameters of an indicia decoder are disclosed herein. An example method includes applying a decoder algorithm to a first image data set to detect and decode one or more indicia, wherein the decoder algorithm utilizes a first set of parameters and a second set of parameters. The example method includes determining a minimum value and a maximum value for each parameter of the first set of parameters, and adjusting a parameter of the second set of parameters from a first value to a second value. The example method includes applying the decoder algorithm to a second image data set to detect and decode one or more indicia, and setting the parameter to one of the first value or the second value during subsequent applications of the decoder algorithm.