Integrated hyperspectral data processing software "LuxFlux"

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last updated：May 24, 2024

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Detecting product defects that are difficult to distinguish with RGB cameras. For quality inspection of semiconductors, films and materials, and FPDs.

This is a general-purpose industrial software focused on data processing centered around hyperspectral imaging (HSI) technology. It enables the development of analysis and measurement models using machine learning, allowing for the visualization, classification, and measurement of objects on manufacturing lines. Currently, the Japanese hyperspectral camera market is primarily centered on academic and research and development applications, but significant growth is expected in the industrial market in the future. Industrial software that processes the vast amount of information outputted from hyperspectral cameras will become increasingly important. 【Features】 - Supports data cube (ENVI_Cube) file input and hyperspectral camera input - Equipped with measurement functions utilizing interference technology in addition to classification and quantification of targets - Allows for the generation of classification and measurement models using machine learning with simple UI operations - Enables real-time classification and measurement using inline execution tools - Compatible with hyperspectral cameras using "push broom," "tunable bandpass filter," and "snapshot mosaic" methods - Available for Windows, Linux, and MacOS 64-bit - Supports Intel, AMD, and ARM *For more details, please refer to the PDF.

Related Link - https://www.canon-its.co.jp/files/user/solution/lu…

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basic information

<Model Learning Environment "fluxTrainer"> A software that allows for wavelength classification (feature extraction) from a large amount of data captured by a hyperspectral camera, enabling model learning and validation using machine learning. It can comprehensively process the vast optical information contained in hundreds of wavelengths within hyperspectral data, which has traditionally been considered difficult. Since model learning using machine learning is possible, user validation tasks are made easier, and data scientists are not required. <Model Implementation Environment "fluxEngine"> An SDK for creating applications that run in real-time based on the learning models generated by "fluxTrainer." It also allows for calibration, target position correction, and integration with controllers and PLCs (programmable logic controllers). Results obtained from production and inspection lines can be tuned in fluxTrainer, making it smoothly applicable to business operations in production sites that were not considered in traditional research and evaluation analysis software. *You can view introductory materials with use cases from the "PDF Download." Please feel free to contact us for inquiries.