Multi-Imager Barcode Reader Contrast-Based Decoding Optimization
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Solution Overview
Problem
Barcode readers with multiple imaging assemblies face increased computational resources and power consumption due to the need to decode barcodes from multiple images, leading to slower scan operations and faster battery drain.
Innovation Solution
A barcode reader system that includes two imaging assemblies with a controller that calculates contrast levels within specific regions of each image and prioritizes decoding operations based on which image has a higher contrast level, optimizing resource allocation and reducing processing time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple imaging assemblies are used to capture barcodes over a wide range of distances, then the working range and versatility of the barcode reader is improved, but the computational resources required and power consumption increase
Solution Approach 1:
The controller performs preliminary contrast level calculations on representative regions of each image before executing full barcode decode operations. This preliminary action identifies which images are most likely to contain decodable barcodes, allowing the system to skip unnecessary decode operations on low-contrast images and thereby reduce power consumption while maintaining wide working range capability
Solution Approach 2:
Instead of performing full decode operations on all images from multiple imaging assemblies, the system performs partial analysis by calculating contrast levels only on specific regions of interest. This partial action reduces the computational burden and energy consumption while still enabling the system to identify and decode barcodes effectively across the wide working range
2Adaptability or versatility
If multiple imaging assemblies are used to capture barcodes over a wide range of distances, then the working range and versatility of the barcode reader is improved, but the device complexity increases
Solution Approach 1:
The controller divides the image processing task by segmenting it into two stages: first calculating contrast levels on representative regions of each image, then selectively performing full decode operations only on images with sufficient contrast. This segmentation simplifies the overall system architecture by adding a lightweight preliminary filtering step that manages the complexity of handling multiple imaging assemblies
Solution Approach 2:
The contrast level calculation serves as an intermediary step between image capture and full barcode decoding. This intermediary process filters images based on their potential decodability, reducing the complexity of managing multiple imaging assemblies by eliminating unnecessary full decode operations on unsuitable images
3Reliability
If barcode decode operations are performed on multiple images from multiple imaging assemblies, then the reliability of successful barcode reading is improved, but the scan operation time increases
Solution Approach 1:
The controller performs preliminary contrast level calculations before executing full barcode decode operations. This preliminary action identifies which images are most likely to contain decodable barcodes, allowing the system to skip unnecessary decode operations and thereby reduce scan operation time while maintaining high reliability of successful barcode reading
Solution Approach 2:
The system uses contrast level measurements as feedback to dynamically determine which images warrant full decode operations. This feedback mechanism ensures that decode resources are allocated to images with the highest probability of containing readable barcodes, optimizing both reliability and speed of scan operations
4Reliability
If barcode decode operations are performed on multiple images from multiple imaging assemblies, then the reliability of successful barcode reading is improved, but the computational resources required increase
Solution Approach 1:
Instead of performing full decode operations on all images from multiple imaging assemblies, the system performs partial analysis by calculating contrast levels only on specific regions of interest. This partial action reduces the computational burden while still enabling the system to identify and decode barcodes effectively, maintaining reliability with reduced resource requirements
Solution Approach 2:
The system extracts and analyzes only the most relevant features (contrast levels in representative regions) from each image before committing to full decode operations. This extraction approach reduces computational resources by focusing only on the critical characteristics needed to determine decode suitability, while maintaining reliable barcode reading through selective processing
Data Source
AI summary
In an embodiment, the present invention is a barcode reader that includes: a first imaging assembly configured to capture a first image; a second imaging assembly positioned relative to the first imaging assembly and configured to capture a second image; and a controller communicatively coupled to the first imaging assembly and the second imaging assembly. The controller is configured to: calculate a first contrast level within a first region within the first image; calculate a second contrast level within a second region within the second image; execute a first barcode-decode operation on the first image when the first contrast level is greater than the second contrast level; and execute the first barcode-decode operation on the second image when the second contrast level is greater than the first contrast level.