Barcode Identification via Multi-Wavelength Weighted Image Combination
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Solution Overview
Problem
Existing barcode identification and decoding methods face challenges in achieving reliable and efficient processing, particularly in scenarios where barcodes overlap with complex backgrounds or are printed using invisible inks, leading to difficulties in contrast enhancement and background separation.
Innovation Solution
The method involves acquiring first and second image data of an object using different illumination wavelengths, calculating a weighting factor based on statistical processing of pixel values, and generating third image data through a weighted combination to enhance contrast and remove background, thereby improving barcode identification and decoding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple illumination wavelengths are used to capture images, then barcode contrast and reliability are improved, but processing time and computational complexity increase
Solution Approach 1:
The patent applies preliminary action by pre-calculating optimal weighting factors based on statistical properties of the multi-wavelength images. These weighting factors are determined before final barcode decoding, allowing the system to prepare optimized combined images in advance. This reduces real-time processing requirements while maintaining high reliability in barcode identification.
Solution Approach 2:
The patent employs parameter changes by dynamically adjusting illumination wavelengths and their corresponding weighting factors based on the specific characteristics of the barcode and background. By optimizing these parameters for different scenarios (e.g., fluorescent inks vs. traditional inks), the system achieves high reliability without uniformly increasing processing time for all cases.
2Measurement precision
If weighted combination of multi-wavelength images is performed, then background separation is improved, but computational complexity increases
Solution Approach 1:
The patent applies the extraction principle by isolating and removing background components from the multi-wavelength images through statistical analysis. By separating background pixel values from barcode signal values and applying appropriate weighting, the system extracts only the relevant barcode information. This reduces computational complexity by eliminating unnecessary background processing while maintaining high background separation precision.
Solution Approach 2:
The patent uses statistical parameters (mean, standard deviation) as intermediaries to bridge the multi-wavelength images and the final combined image. These statistical measures serve as mediators that simplify the complex task of background separation by providing quantitative criteria for weighting and combining images, thereby reducing computational complexity while maintaining precision.
3Measurement precision
If statistical processing is applied to pixel values, then contrast enhancement is improved, but processing speed decreases
Solution Approach 1:
The patent applies preliminary action by performing statistical processing (calculating means, standard deviations, and weighting factors) on image regions before final barcode decoding. By pre-processing and optimizing contrast in advance, the system reduces the computational burden during real-time decoding, thereby maintaining high contrast enhancement quality while improving overall processing speed.
Solution Approach 2:
The patent employs partial action by applying statistical processing selectively to specific image regions or pixels that require contrast enhancement, rather than uniformly processing the entire image. This targeted approach maintains high contrast quality for critical areas while reducing unnecessary computations in already well-contrasted regions, thus improving processing speed.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach effectively enhances barcode contrast and reliability by equalizing background pixel values, facilitating more accurate decoding even in complex scenarios, such as those involving fluorescent or invisible inks, and improving processing speed.
Implementation Method 1
the specific case that fluorophores are employed
Data Source
Figure 1A~2B
Figure 3A~3E
Figure 4A~4C
AI summary
A method for identifying a one- or two-dimensional barcode in input image data, the method comprising the steps of: obtaining first image data of a first image of the object, said first image being acquired using a first illumination wavelength; obtaining second image data of a second image of the object, said second image being acquired using a second illumination wavelength being different from said first illumination wavelength; calculating a weighting factor based on a statistical processing of pixel values of the first image data and pixel values of the second image data; and generating third image data by calculating a weighted combination using the pixel values of said first image data, the pixel values of said second image data, and said weighting factor.