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Modelling and Simulation of Neuromorphic Datasets for Anomaly Detection in Computer Vision
arXiv – CS AI|Mike Middleton, Teymoor Ali, Hakan Kayan, Basabdatta Sen Bhattacharya, Charith Perera, Oliver Rhodes, Elena Gheorghiu, Mark Vousden, Martin A. Trefzer||5 views
🤖AI Summary
Researchers introduce ANTShapes, a Unity-based simulation framework that generates synthetic neuromorphic vision datasets to address the scarcity of Dynamic Vision Sensor data. The tool creates configurable 3D scenes with randomly-behaving objects for training anomaly detection and object recognition systems in event-based computer vision.
Key Takeaways
- →ANTShapes addresses the fundamental challenge of limited Dynamic Vision Sensor datasets in neuromorphic computer vision research
- →The Unity-based framework generates synthetic datasets with configurable 3D scenes and statistically-labeled anomalous behaviors
- →Researchers can create unlimited dataset samples by adjusting simple parameters within the software
- →The tool supports multiple computer vision applications including object recognition, localization, and anomaly detection
- →Statistical sampling follows central limit theorem principles for reliable anomaly labeling
#neuromorphic-computing#computer-vision#anomaly-detection#dataset-simulation#machine-learning#research-tools#synthetic-data#unity-engine
Read Original →via arXiv – CS AI
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