Tint Analyser
ModnyCo successfully developed a machine learning-based algorithm for accurately detecting the density and colour of tinted lenses. This innovative algorithm, designed to emit controlled visible wavelengths and measure attenuation levels, utilized a trained classifier and a non-linear estimation model for precise tint identification. Extensive experiments with various lenses trained the system, while implemented calibration techniques ensured consistent performance across different devices. The project’s highlight was its dynamic colour definition feature, providing clients with the flexibility to customize colour space analysis, showcasing our commitment to pioneering adaptable, technology-driven solutions in optical analysis.
At ModnyCo, we embarked on a groundbreaking project that harnessed the power of machine learning to redefine the analysis of tinted lenses. This bespoke algorithm was engineered to emit controlled visible wavelengths, a process that was crucial for measuring the attenuation levels of light, at different wavelength, as it passed through different lenses. The sophistication of this system lay in its ability to discern subtle variations in tint density and colour, a task that traditionally posed significant challenges.
A critical phase of this project involved extensive experimental training and data collection. We employed multiple devices and a variety of accurately tinted lenses, encompassing a wide spectrum of colours and tint levels. This experimental setup was instrumental in training the classifier, providing a rich dataset from which the machine learning algorithm could learn and improve.
However, during these experiments, we identified performance variations across devices. To address this, we implemented a series of calibration techniques. These techniques were designed to minimize the discrepancies between devices, ensuring consistent and reliable readings regardless of the specific equipment used.
Moreover, a unique aspect of our algorithm was its dynamic colour definition capability. This feature allowed clients to define their own colour spaces, independent of the trained classifier's initial programming. This level of customization was a significant advancement, offering unprecedented flexibility and adaptability to meet diverse client needs.
This project at ModnyCo stands as a testament to our expertise in merging advanced technology with practical applications, particularly in the field of optical analysis. By leveraging machine learning, we've not only enhanced the accuracy of tinted lens analysis but also provided a platform that offers both precision and customization to our clients.
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