Recent research in the field of computer vision strongly focuses on deep learning architectures to tackle image processing problems. Deep neural networks are often considered in complex image processing scenarios since traditional computer vision approaches are expensive to develop or reach their limits due to complex relations. However, a common criticism is the need for large annotated datasets to determine robust parameters. Annotating images by human experts is time-consuming, burdensome, and expensive. Thus, support is needed to simplify annotation, increase user efficiency, and annotation quality. In this paper, we propose a generic workflow to assist the annotation process and discuss methods on an abstract level. Thereby, we review the possibilities of focusing on promising samples, image pre-processing, pre-labeling, label inspection, or post-processing of annotations. In addition, we present an implementation of the proposal by means of a developed flexible and extendable software prototype nested in hybrid touchscreen/laptop device.
翻译：计算机愿景领域最近的研究主要侧重于解决图像处理问题的深层次学习结构。深神经网络常常在复杂的图像处理情景中被考虑,因为传统的计算机愿景方法由于复杂的关系而开发或达到其极限代价高昂。然而,一个共同的批评是需要大型附加说明的数据集来确定稳健的参数。人类专家的图像说明费时、繁琐和昂贵。因此,需要支持简化批注、提高用户效率和批注质量。在本文件中,我们提议了一个通用工作流程,以协助批注过程,并在抽象的层面上讨论方法。因此,我们审查了侧重于有希望的样本、图像预处理、预贴标签、标签检查或事后处理说明的可能性。此外,我们介绍了通过开发一种灵活和可扩展的软件原型嵌入混合触摸屏/触摸式装置来实施该提案的情况。
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