Guardado en:
Detalles Bibliográficos
Autor principal: Sarfati, Raphael
Formato: Preprint
Publicado: 2024
Materias:
Acceso en línea:https://arxiv.org/abs/2410.19932
Etiquetas: Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
_version_ 1866929561122373632
author Sarfati, Raphael
author_facet Sarfati, Raphael
contents Identifying firefly flashes from other bright features in nature images is complicated. I provide a training dataset and trained neural networks for reliable flash classification. The training set consists of thousands of cropped images (patches) extracted by manual labeling from video recordings of fireflies in their natural habitat. The trained network appears as considerably more reliable to differentiate flashes from other sources of light compared to traditional methods relying solely on intensity thresholding. This robust tracking enables a new calibration-free method for the 3D reconstruction of flash occurrences from stereoscopic 360-degree videos, which I also present here.
format Preprint
id arxiv_https___arxiv_org_abs_2410_19932
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Tracking and triangulating firefly flashes in field recordings
Sarfati, Raphael
Computer Vision and Pattern Recognition
Identifying firefly flashes from other bright features in nature images is complicated. I provide a training dataset and trained neural networks for reliable flash classification. The training set consists of thousands of cropped images (patches) extracted by manual labeling from video recordings of fireflies in their natural habitat. The trained network appears as considerably more reliable to differentiate flashes from other sources of light compared to traditional methods relying solely on intensity thresholding. This robust tracking enables a new calibration-free method for the 3D reconstruction of flash occurrences from stereoscopic 360-degree videos, which I also present here.
title Tracking and triangulating firefly flashes in field recordings
topic Computer Vision and Pattern Recognition
url https://arxiv.org/abs/2410.19932