Geometric Image Cropping for Faster Barcode Processing
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Solution Overview
Problem
Current image processing technologies for barcode scanners and similar devices are inefficient in cropping images to focus on relevant machine-readable symbologies, leading to increased processing time and resource wastage due to the inclusion of non-relevant data, especially at varying distances from the target object.
Innovation Solution
Implementing geometric image cropping based on cropping parameters derived from ranging data and lens information, using raytracing or magnification estimation to efficiently crop captured images, reducing processing time and resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If geometric image cropping is implemented using raytracing and magnification estimation, then image processing efficiency is improved and processing time is reduced, but device complexity increases due to additional cropping parameter identification steps
Solution Approach 1:
The patent pre-calculates and stores cropping parameters in lookup tables based on ranging data and lens information before actual image processing occurs. This preliminary preparation allows the system to quickly retrieve and apply cropping parameters during real-time operation, avoiding complex calculations at processing time while maintaining high efficiency
Solution Approach 2:
The patent introduces cropping parameters as an intermediary element that mediates between the raw captured image and the final processed image. These parameters, derived from ranging data and lens characteristics through raytracing or magnification estimation, enable precise cropping without requiring complex real-time computation, thus resolving the contradiction between efficiency and complexity
2Measurement precision
If full captured images are processed without cropping, then measurement precision is maintained, but processing time increases and computing resources are wasted
Solution Approach 1:
The patent extracts only the relevant portion of the captured image containing the machine-readable symbology by applying cropping parameters. This extraction process removes non-relevant data from the image while preserving the critical symbology region, thereby reducing processing time and resource consumption without compromising detection accuracy
Solution Approach 2:
The patent applies different quality levels to different regions of the image by cropping to focus computational resources on the symbology-containing region. The cropping parameters ensure that the local region of interest maintains full measurement precision while peripheral non-relevant areas are excluded from processing
3Adaptability or versatility
If cropping parameters are dynamically adjusted based on ranging data and lens information, then adaptability is improved, but device complexity increases due to additional computational requirements
Solution Approach 1:
The system pre-computes cropping parameters for various ranging data values and lens configurations, storing them in lookup tables. This preliminary action enables dynamic adaptability without requiring complex real-time calculations, as the system simply retrieves pre-prepared parameters based on current operating conditions
Solution Approach 2:
The patent adjusts cropping parameters based on changes in ranging data and lens information by selecting appropriate pre-computed parameters from lookup tables. This parameter change approach maintains high adaptability to different scanning conditions while avoiding the computational complexity of dynamic recalculation
Data Source
AI summary
Embodiments of the present disclosure relate to utilizing geometric image cropping for improved image processing. Such geometric image cropping improves efficiency and/or throughput of various image processing tasks, for example for reading a machine-readable symbology via a specially-configured scanner. Some embodiments generate cropping parameter(s) using raytracing projections from lens data and ranging data for use in cropping image(s). Some embodiments generate cropping parameter(s) using magnification estimation for use in cropping image(s). Generated cropping parameter(s) may be stored via a reader, for example to a range-parameter table, to efficiently be retrieved and utilized for cropping subsequently captured images while remaining accurate and efficient for image processing.