Target Barcode Detection Under Blur Using Multi-Image Similarity
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Solution Overview
Problem
Existing methods for barcode scanning using standard cameras face challenges such as out-of-focus images, motion blur, poor resolution due to spatial sampling frequency, lens and perspective distortion, rolling shutter effect, and glare, which limit the distance and accuracy of barcode decoding.
Innovation Solution
The use of interpolation functions to determine whether an image contains a target barcode by comparing extracted vector data with target data, even in degraded or partially occluded conditions, using techniques like barcode interpolation and similarity measurement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If standard cameras are used to scan barcodes, then device complexity is reduced, but measurement precision and reliability deteriorate due to out-of-focus images, motion blur, and poor resolution
Solution Approach 1:
The system performs preliminary actions by capturing multiple images at different focal distances before decoding. The image selection module pre-processes images by comparing focus metrics and selecting the sharpest image, thereby preparing optimal input data for the decoding module to achieve reliable results despite using standard cameras
Solution Approach 2:
The system creates multiple copies of the barcode capture process by taking multiple images at different focal distances. This allows the system to have redundant copies of the same barcode information captured under different conditions, from which the best quality copy can be selected for decoding
2Productivity
If the distance between camera and barcode is increased, then scanning speed and productivity improve, but measurement precision deteriorates due to spatial sampling frequency limitations
Solution Approach 1:
The system performs preliminary focus assessment on multiple images captured at different distances. By pre-evaluating image quality metrics and selecting the optimal image before decoding, the system can capture barcodes at greater distances while maintaining precision through intelligent selection rather than relying solely on fixed focal length
3Loss of time
If traditional barcode decoding methods are used on degraded images, then processing time is reduced, but reliability deteriorates due to inability to handle out-of-focus or blurred images
Solution Approach 1:
The system performs preliminary image quality assessment and selection before the main decoding process. By pre-identifying the sharpest image among multiple captures, the system ensures that the decoding module receives optimal input, thereby maintaining high reliability without significantly increasing overall processing time
Solution Approach 2:
The system implements feedback by evaluating image quality metrics (sharpness, focus) and using this information to select the best image for decoding. This feedback loop ensures that only images meeting quality thresholds are processed further, maintaining reliability while avoiding wasted processing on degraded images
4Measurement precision
If laser scanners are used instead of standard cameras, then measurement precision and reliability improve, but device complexity and cost increase
Solution Approach 1:
The system uses standard cameras to create multiple image copies at different focal distances, effectively replicating the function of a laser scanner's precise focal plane selection. This approach achieves similar reliability to laser scanners but with simpler, more versatile standard camera equipment
Solution Approach 2:
The system changes the parameter of focal distance by capturing images at multiple distances rather than using a fixed focal length. This parameter variation allows standard cameras to achieve the precision normally requiring specialized laser scanner hardware, thereby reducing device complexity while maintaining measurement precision
Data Source
AI summary
Systems and methods are provided for determining whether an image or location contains a barcode. The systems and methods include comparing visual data extracted from an image to target visual data. The systems and methods also include comparing different portions of captured barcode data to corresponding portions of target barcode data. The systems and methods also include utilizing barcode formatting data when comparing or computing a similarity measure. The systems and methods also include causing a second image to be captured when no match is found in a first image. The systems and methods also include identifying a target barcode from a barcode database based on spatial information about where an image was captured. The systems and methods also include validating a barcode based on different regions of the barcode from two different images of the barcode. The systems and methods also include using two barcodes captured in an image.