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Astrocyte
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Based on artificial intelligence, Astrocyte is an application that is designed to train neural networks on 2D images for a wide variety of applications. With an interface that delivers impressive flexibility, users can use their own image samples to train neural networks for the purpose of unique object detection/classification, noise reduction and segmentation. Astrocyte offers visualising and interpreting models for optimised performance and reliability. Furthermore, you can export these models for later use at runtime using Teledyne Dalsa's Sapera and Sherlock platforms. |
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Key Features
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• Supports Microsoft Windows (Linux to
come)
• Pre-trained models for reduced training effort • Export of model file to interface with Sapera Processing and Sherlock for runtime inference • Visualization of model performance with numerical metrics and heatmaps. • Graphical view of training progress with possibility of cancelling/resuming sessions |
• Access to hyperparameters for highly
flexible training, including selection of neural network type.
• Import of training samples from local or remote locations with various file selection schemes. • Multiple deep learning architectures for a wide range of applications. • Training deployed on user PC for full privacy (no need to share data on cloud). |
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| Noise Reduction | Segmentation | |||
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| Noise reduction intends to deliver a higher-quality image from its original state. It is an important component in applications such as digital photography, medical image analysis, remote sensing, surveillance and digital entertainment. |
Image segmentation is important component in computer vision and is used for defect sorting/qualification, food sorting, shape analysis, etc. Image segmentation involves dividing input image into segments to simplify image analysis.
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| Object Detection | Object Classification | |||
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| Object Detection involves identifying one or more objects of interest in an image. Object Detection is used to solve problems like presence detection, object tracking, defect localisation and sorting, etc. | Classification involves predicting which class an item belongs to. Classification is used to solve problems like defect identification, character recognition, presence detection, food sorting, etc. | |||
| Anomaly Detection | ||||
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| Anomaly Detection is the identification of rare occurrences, items or events of concern due to their differing characteristics from majority of the processed data. Anomaly Detection is a binary classifier dedicated to identifying good and bad samples. Unlike regular classification Anomaly Detection can train on unbalanced datasets (i.e. large number of good samples and small number of bad samples). Anomaly Detection is used on any application involving identification of defects on a surface or scene. | ||||
Click here to download the Astrocyte datasheet |
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