Gemstone Identification via 3D Spectral Mapping and Siamese Neural Networks
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Solution Overview
Problem
Current methods for gemstone identification, such as Fourier Transform Infrared Spectroscopy (FTIR), face challenges in accurately distinguishing between gemstones due to averaging signals from entire volumes or limited probe volumes, which can obscure unique spectral signatures and make it difficult to trace the origin and provenance of gemstones, especially when they are cut into smaller pieces.
Innovation Solution
The method involves obtaining multiple spectral measurements from different probe volumes to construct a 3D spectral map, combined with machine learning using a Siamese neural network to identify gemstones by maximizing the distance between different gemstones and minimizing the distance between measurements of the same gemstone, allowing for accurate and non-invasive identification and tracing of gemstones throughout their production and distribution process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Volume of moving object
If spectral data is obtained from the entire volume of a gemstone, then the measurement covers the whole object, but unique spectral signatures are obscured due to signal averaging
Solution Approach 1:
The gemstone is divided into multiple discrete probe volumes or regions, each measured separately to capture unique spectral signatures. The system selectively measures specific regions rather than averaging the entire volume, preserving distinctive spectral characteristics that would otherwise be obscured.
2Measurement precision
If multiple spectral measurements are taken from different regions, then unique spectral signatures are preserved, but the device complexity and measurement time increase
Solution Approach 1:
The system performs preliminary scanning or mapping to identify regions of interest containing unique spectral signatures before conducting detailed measurements. This preliminary action guides subsequent focused measurements, reducing the number of regions that need to be measured and simplifying the overall measurement process.
Solution Approach 2:
The measurement system automatically identifies and selects regions with unique spectral characteristics without requiring manual intervention or complex configuration. The system self-adjusts the measurement strategy based on the gemstone's inherent spectral properties, reducing operational complexity.
3Ease of operation
If traditional spectroscopy methods are used, then the process is simple, but identification accuracy is insufficient for distinguishing similar gemstones
Solution Approach 1:
A machine learning model serves as an intermediary between the spectral measurements and the identification result. The model processes the complex spectral data and patterns, automatically distinguishing between similar gemstones while maintaining ease of operation. The system remains simple to use, but the intermediary model provides enhanced identification accuracy.
Data Source
AI summary
Identifying 3D objects A method for identification of 3D objects comprises illuminating at least part of a 3D object with electromagnetic radiation, spectroscopically obtaining spectral data for one or more regions of the 3D object, and generating, at a data processing apparatus, an identification result for the 3D object using a trained machine learning model. The trained machine learning model processes the obtained spectral data for the one or more regions to generate one or more model outputs from which the identification result is derived.