SUPERSEDED - Photometric stereo dataset with annotated leaf masks

## This item has been replaced by the one which can be found at: https://doi.org/10.7488/ds/2514 ## Automated leaf segmentation is a challenging area in computer vision. Recent advances in machine learning approaches allowed to achieve better results than traditional image processing techniques (Scharr et al., 2016; Mengye and Zemel, 2016); however, training such systems often require large annotated datasets (Giuffrida et al., 2017). To contribute with annotated datasets and help to overcome this bottleneck in plant phenotyping research, here we provide a novel photometric stereo (PS) dataset with annotated leaf masks. This dataset forms part of work done in the BBSRC Tools and Resources Development project BB/N02334X/1.

本条目已由可通过以下链接获取的版本替代：https://doi.org/10.7488/ds/2514。自动化叶片分割是计算机视觉领域极具挑战性的研究方向。近年来机器学习方法的进展使得相关研究成果优于传统图像处理技术（Scharr等，2016；Mengye与Zemel，2016），但训练此类系统通常需要大规模带标注数据集（Giuffrida等，2017）。为了提供带标注数据集以助力攻克植物表型研究中的这一瓶颈，本文发布一款全新的光度立体视觉（photometric stereo, PS）带叶片掩码标注数据集。本数据集是BBSRC工具与资源开发项目BB/N02334X/1的研究成果之一。