Hyperspectral Symbol Reader for Material Differentiation
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Solution Overview
Problem
Conventional machine-readable symbol readers face challenges in differentiating similar symbols and distinguishing symbols from detailed backgrounds, due to limited inputs and color differentiation capabilities.
Innovation Solution
Incorporating hyperspectral imaging cameras and sensors that capture 3D cubic data sets, allowing for analysis across a wide spectrum of light to differentiate between materials and decode symbols more accurately, even when they appear the same color in the visible spectrum.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional machine-readable symbol readers use standard imaging sensors, then the device complexity is low, but the measurement precision is insufficient to differentiate similar symbols and distinguish symbols from detailed backgrounds
Solution Approach 1:
The patent transitions from standard 2D imaging to hyperspectral 3D data cubes by adding a spectral dimension. Each pixel becomes a spectrum across multiple wavelengths, enabling material differentiation that goes beyond visible color. This dimensional expansion provides the measurement precision needed to distinguish similar symbols while maintaining the ability to process detailed backgrounds.
Solution Approach 2:
The system changes the measurement parameters from single-wavelength intensity detection to multi-wavelength spectral analysis. By capturing data across a spectrum of wavelengths rather than a single color, the system gains the precision to differentiate materials that appear identical in visible light, directly addressing the measurement precision requirement.
2Ease of operation
If machine-readable symbol readers use flood illumination to illuminate the entire symbol, then the ease of operation is improved, but the ability to differentiate similar symbols and distinguish from backgrounds is reduced
Solution Approach 1:
The patent segments the illumination approach by using multiple light sources at different wavelengths rather than a single flood light. Each wavelength channel provides a separate measurement perspective, allowing the system to maintain ease of operation while achieving superior material differentiation through spectral analysis of the segmented data.
Solution Approach 2:
The hyperspectral sensor acts as an intermediary that transforms the illumination data into spectral information. It captures the interaction between light and material across multiple wavelengths, enabling the system to differentiate materials that would be indistinguishable with conventional single-wavelength illumination while preserving operational simplicity.
3Reliability
If conventional readers capture only visible spectrum data, then the device complexity is low, but the reliability is insufficient to detect material differences and verify product quality
Solution Approach 1:
The hyperspectral sensor system provides multi-functionality by enabling both standard visible spectrum imaging and extended spectral analysis. This universal approach allows the same device to perform conventional reading tasks while simultaneously detecting material differences and verifying product quality through spectral characteristics, thereby improving reliability without requiring separate specialized systems.
Solution Approach 2:
The system uses composite data structures combining multiple wavelength channels to create comprehensive material signatures. By integrating spectral information across different wavelengths, the system achieves reliable detection of material differences and product quality verification, with the composite data providing both differentiation capability and verification accuracy.
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 enhances decoding performance and improves the ability to verify product quality and authenticity by distinguishing between inks and materials that appear identical in the visible spectrum, reducing errors and improving efficiency in symbol reading.
Implementation Method 1
Machine-readable symbols are typically composed of patterns of high and low reflectance areas
Implementation Method 2
hyperspectral imaging cameras and sensors that capture 3D cubic data sets, allowing for analysis across a wide spectrum of light to differentiate between materials
Data Source
AI summary
A machine-readable symbol reader can capture a cubic data set of an object carrying a machine-readable symbol. The machine-readable symbol reader can include a hyperspectral sensor, and the cubic data set may include image data for the machine-readable symbol within multiple, different wavelengths of the electromagnetic spectrum. The cubic data may be separated into portions that are each analyzed by specific processors, and those separate analyses can be used to increase the capabilities and efficiency compared to known machine-readable symbol readers.