Geometric Image Cropping for Faster Symbology Processing
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Solution Overview
Problem
Current image processing technologies for barcode scanners and readers are inefficient in cropping images to focus on relevant machine-readable symbologies, leading to increased processing time and resource wastage due to 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 image raytracing functions to quickly identify and crop the area containing the machine-readable symbology, reducing processing time and resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If geometric image cropping is implemented using ranging data and image raytracing functions, then image processing time is reduced by a factor of five and computing resources are minimized, but device complexity increases due to the need for ranging data processing and geometric calculations
Solution Approach 1:
The system performs preliminary geometric calculations and cropping parameter determinations before actual image processing. By pre-computing the crop region based on ranging data and image raytracing functions, the system prepares the image data in advance, reducing the time required for subsequent processing operations and achieving faster overall image processing.
Solution Approach 2:
The invention extracts only the relevant portion of the captured image for processing by determining a crop region based on geometric calculations. This extraction approach removes unnecessary data from the image processing pipeline, reducing computational load and processing time while maintaining only the essential information needed for barcode detection and decoding.
2Device complexity
If the entire captured image is processed without geometric cropping, then device complexity remains low, but processing time increases and computing resources are wasted due to non-relevant data
Solution Approach 1:
The system applies different processing strategies to different regions of the captured image. By determining a specific crop region where the machine-readable symbology is likely located and processing only that region with high-quality algorithms, while handling the rest of the image with simpler operations or skipping processing entirely, the system optimizes the balance between processing thoroughness and computational efficiency.
Data Source
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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.