作者
Oscar Beijbom, Tali Treibitz, David I Kline, Gal Eyal, Adi Khen, Benjamin Neal, Yossi Loya, B Greg Mitchell, David Kriegman
发表日期
2016/3/29
期刊
Scientific reports
卷号
6
期号
1
页码范围
23166
出版商
Nature Publishing Group UK
简介
Large-scale imaging techniques are used increasingly for ecological surveys. However, manual analysis can be prohibitively expensive, creating a bottleneck between collected images and desired data-products. This bottleneck is particularly severe for benthic surveys, where millions of images are obtained each year. Recent automated annotation methods may provide a solution, but reflectance images do not always contain sufficient information for adequate classification accuracy. In this work, the FluorIS, a low-cost modified consumer camera, was used to capture wide-band wide-field-of-view fluorescence images during a field deployment in Eilat, Israel. The fluorescence images were registered with standard reflectance images, and an automated annotation method based on convolutional neural networks was developed. Our results demonstrate a 22% reduction of classification error-rate when using both …
学术搜索中的文章
O Beijbom, T Treibitz, DI Kline, G Eyal, A Khen, B Neal… - Scientific reports, 2016