For art investigations of paintings, multiple imaging technologies, such as visual light photography, infrared reflectography, ultraviolet fluorescence photography, and x-radiography are often used. For a pixel-wise comparison, the multi-modal images have to be registered. We present a registration and visualization software tool, that embeds a convolutional neural network to extract cross-modal features of the crack structures in historical paintings for automatic registration. The graphical user interface processes the user's input to configure the registration parameters and to interactively adapt the image views with the registered pair and image overlays, such as by individual or synchronized zoom or movements of the views. In the evaluation, we qualitatively and quantitatively show the effectiveness of our software tool in terms of registration performance and short inference time on multi-modal paintings and its transferability by applying our method to historical prints.
翻译:对于绘画的艺术调查,经常使用多种成像技术,如视觉光摄影、红外反射、紫外线荧光摄影和X射线摄影等。为了进行像素比较,必须登记多式图像。我们展示了一个注册和可视化软件工具,其中嵌入了一个革命神经网络,以提取历史绘画中的裂缝结构的跨式特征,进行自动登记。图形用户界面处理用户的投入,以配置登记参数,并交互调整图像视图与已登记的对子和图像覆盖的图像视图,例如通过个人或同步的缩放或视图移动。在评估中,我们从质量和数量上展示了我们的软件工具在登记绩效和多式绘画的短推引时间方面的有效性,并通过对历史指纹应用我们的方法将其转移。