Speckle Analysis for Material Characterization
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Solution Overview
Problem
Current methods for analyzing speckles generated on a material's surface by a coherent light beam are not precise enough to differentiate between various materials, including biological and non-biological tissues, and fail to accurately characterize the material forming the surface.
Innovation Solution
A method involving the projection of a coherent light beam onto a surface to generate speckles, followed by image acquisition and processing using autocorrelation and wavelet transforms to calculate characterization criteria, such as eigenvalues and phase shifts, allowing for the determination of the material's properties and discrimination between different materials.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If simple speckle image analysis is used, then the process is simple and fast, but the measurement precision is insufficient to differentiate between various materials
Solution Approach 1:
The patent segments the image processing into distinct functional components: autocorrelation function calculation for intensity analysis, wavelet transform for phase analysis, and eigenvalue computation for mathematical characterization. This segmentation allows each component to specialize in analyzing specific material properties, improving overall precision while maintaining manageable complexity through modular processing stages.
Solution Approach 2:
The patent transforms the two-dimensional speckle image into multiple dimensional characterizations by computing autocorrelation functions that reveal spatial frequency information, wavelet transforms that expose phase distribution patterns, and eigenvalues that provide mathematical descriptors. This dimensional transformation enables differentiation between materials that appear similar in the original image, significantly improving measurement precision.
2Measurement precision
If autocorrelation and wavelet transforms are applied to analyze speckles, then material characterization precision is improved, but the calculation time and processing complexity increase
Solution Approach 1:
The patent performs preliminary computations by pre-calculating and storing autocorrelation functions and wavelet transform parameters for different material types during a training phase. During actual material identification, these pre-computed references are compared against the test sample, significantly reducing processing time while maintaining high precision. The complex mathematical operations are performed in advance when time is not critical.
Solution Approach 2:
The patent extracts only the most informative features from the complex speckle patterns - specifically the autocorrelation function characteristics and wavelet transform coefficients - rather than processing the entire image data. This feature extraction approach retains the essential material differentiation information while dramatically reducing the computational burden and processing time required for material characterization.
3Adaptability or versatility
If only gray shade analysis is used, then the method is simple, but it can only differentiate between biological tissues and cannot characterize non-biological materials
Solution Approach 1:
The patent creates a universal material characterization method that works across multiple material types (biological tissues, non-biological materials, synthetic substances) by employing multiple analysis techniques: autocorrelation for intensity patterns, wavelet transforms for phase distributions, and eigenvalue decomposition for mathematical characterization. This multi-functional approach enables the same processing pipeline to differentiate between any material types, achieving both broad adaptability and high precision.
Solution Approach 2:
The patent changes the analysis parameters from simple gray shade intensity to multiple dimensional parameters including autocorrelation function values, wavelet transform coefficients, eigenvalues, and phase distribution statistics. These parameter transformations reveal material-specific characteristics that are not visible in the original image, enabling precise differentiation between biological and non-biological materials as well as between different non-biological materials.
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 method enables accurate characterization of materials by analyzing speckle patterns, distinguishing between a wide range of materials based on their diffusive properties and surface relief, improving the precision of material identification and fraud detection in biometric applications.
Implementation Method 1
generate speckles on the surface resulting from interference of light rays scattered by the surface
Implementation Method 2
interference of light rays scattered by the surface
Implementation Method 3
the calculation of an autocorrelation function of the light intensity on the image
Implementation Method 4
the calculation of a wavelet transform of the image, and the calculation of the value of said criterion from among the following group: average of the phases of the wavelet coefficients on the image, or standard deviation of the phases of the wavelet coefficients on the image
Data Source
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AI summary
The invention proposes a method for characterizing a material in which a surface is formed, comprising the steps of: - projecting a coherent light beam onto the surface, to generate on said surface speckles resulting from interference of the light rays scattered by said surface, - acquiring an image of said surface on which the speckles appear, - processing said image to calculate at least one criterion for characterizing the material, and - from the criteria, determining the material constituting the surface, in which the image processing step comprises: - calculating an autocorrelation function of the light intensity on the image and calculating the value of at least one criterion established from said function, and - calculating the value of at least one criterion representative of a phase distribution of the light rays scattered by the surface.