Computer Vision Authentication for Material Samples
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Solution Overview
Problem
Current methods for authenticating materials or products suspected of being counterfeit are time-consuming and require laboratory testing, which is invasive and not proactive.
Innovation Solution
A computer-implemented method and system that uses digital images to extract computer-vision features, trains a classification algorithm, and evaluates test samples to determine authenticity or batch membership without laboratory testing or product alteration, utilizing a mobile device with a graphical user interface and cloud computing for processing and decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional laboratory testing methods are used to authenticate materials, then measurement precision and reliability are improved, but loss of time and productivity deteriorate significantly
Solution Approach 1:
The patent replaces the mechanical laboratory testing system with an optical computing system that uses digital images and computer vision algorithms. The system captures images of material samples and processes them through machine learning models to authenticate materials, eliminating the need for physical laboratory analysis while maintaining accuracy.
Solution Approach 2:
The patent creates a digital copy of the material sample through imaging, then analyzes this copy using computational methods. The digital image serves as a surrogate for the physical sample, allowing authentication to be performed on the copy rather than requiring direct manipulation of the original material in a laboratory setting.
2Measurement precision
If traditional laboratory testing is used, then authentication accuracy is improved, but device complexity and ease of operation worsen due to specialized equipment requirements
Solution Approach 1:
The patent employs a universal computing platform that can handle multiple authentication tasks through software algorithms rather than requiring specialized hardware for each type of material analysis. The same computational system can authenticate different materials by loading appropriate machine learning models, eliminating the need for multiple specialized laboratory instruments.
Solution Approach 2:
Complex mechanical laboratory equipment is replaced with a computational system that uses standard digital imaging combined with machine learning algorithms. The complexity is shifted from physical hardware to software processing, allowing standard devices to perform specialized authentication functions.
3Measurement precision
If invasive laboratory testing is performed, then measurement precision is improved, but object-affected harmful factors increase due to sample alteration
Solution Approach 1:
The patent analyzes a digital copy of the material sample rather than the physical sample itself. The imaging process creates a non-invasive representation that can be examined computationally without touching or altering the original material, eliminating harmful effects on the sample.
Solution Approach 2:
Physical manipulation of samples in laboratory settings is replaced with optical imaging and computational analysis. The system uses light to capture information and algorithms to process it, avoiding mechanical contact that could alter or damage the sample.
4Measurement precision
If manual expert inspection is used, then measurement precision is improved, but productivity and loss of time deteriorate
Solution Approach 1:
The patent implements an automated system that performs authentication independently without requiring human experts for each analysis. The machine learning models automatically process images and make authentication decisions, enabling the system to serve itself and handle multiple samples simultaneously without human intervention.
Solution Approach 2:
The system pre-trains machine learning models using extensive datasets before deployment. This preliminary training action enables the system to perform rapid automated authentication without requiring expert intervention during actual testing, significantly increasing throughput while maintaining accuracy.
Data Source
AI summary
Systems and methods for authenticating material samples are provided. Digital images of the samples are processed to extract computer-vision features, which are used to train a classification algorithm. The computer-vision features of a test sample are evaluated by the trained classification algorithm to identify the test sample.