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C3I-SynMicrosaccade: Una tubería y un conjunto de datos para el reconocimiento de microsaccade utilizando flujos de
Waseem Shariff1, Timothy Hanley2, Maciej Stec1
1School of Electrical and Electronics Engineering, University of Galway, Ireland.
Data in brief
|February 9, 2026
Resumen
Se creó un nuevo conjunto de datos sintéticos de microsacadas (pequeños y rápidos movimientos oculares) para el modelado basado en eventos. Este conjunto de datos equilibrado ayuda en el reconocimiento de microsacadas y la investigación de la visión neuromórfica.
Área de la Ciencia:
- Visión por ordenador Visión por ordenador Visión por ordenador Visión por ordenador Visión por ordenador
- La neurociencia es la neurociencia.
- Ingeniería Biomédica Ingeniería Biomédica.
Sus antecedentes:
- Las microsácadas son cruciales para la percepción visual y son difíciles de modelar.
- Los conjuntos de datos existentes pueden carecer de las características específicas necesarias para el análisis basado en eventos.
Objetivo del estudio:
- Introducir el conjunto de datos C3I-SynMicrosaccade para el modelado y la clasificación de microsacadas basadas en eventos.
- Proporcionar un conjunto de datos a gran escala y equilibrado para entrenar y evaluar algoritmos.
Principales métodos:
- Se generaron secuencias RGB de alta resolución de microsacadas utilizando Blender con trayectoria y clases angulares específicas.
- Convirtió cuadros RGB a flujos de datos basados en eventos utilizando el simulador v2e, imitando sensores neuromórficos.
- Duración temporal controlada y recuento de eventos para el equilibrio de clases y eliminación de sesgos.
Principales resultados:
- Creó un conjunto de datos sintéticos de 175.000 secuencias de eventos para microsacadas.
- El conjunto de datos presenta siete clases angulares distintas (0.5° a 2.0°) con dinámicas de movimiento controladas.
- Asegurado equilibrio de clase, eliminando el sesgo hacia la magnitud del movimiento.
Conclusiones:
- El conjunto de datos C3I-SynMicrosaccade ofrece un marco controlado y reproducible para el estudio de los movimientos oculares de fijación.
- Permite una evaluación robusta de algoritmos basados en eventos bajo dinámicas de movimiento fino.
- Facilita la investigación en el reconocimiento de microsacadas, la visión neuromórfica y el aprendizaje de transferencia de sintético a real.
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