Deep learning for historical books: classification of printing technology for digitized images

Printing technology has evolved through the past centuries due to technological progress. Within Digital Humanities, images are playing a more prominent role in research. For mass analysis of digitized historical images, bias can be introduced in various ways. One of them is the printing technology originally used. The classification of images to their printing technology e.g. woodcut, copper engraving, or lithography requires highly skilled experts. We have developed a deep learning classification system that achieves very good results. This paper explains the challenges of digitized collections for this task. To overcome them and to achieve good performance, shallow networks and appropriate sampling strategies needed to be combined. We also show how class activation maps (CAM) can be used to analyze the results.

Cluttering Reduction for Interactive Navigation and Visualization of Historical Images

Iconographic representations, such as historical photos of geographic spaces, are precious cultural heritage resources capable of describing a particular geographical area’s evolution over time. These photographic collections may vary in size, between hundreds and thousands of items. With the advent of the digital era, many of these documents have been digitized, spatialized, and are available online. Browsing through these digital image collections represents new challenges. This paper examines the topic of historical image exploration in a virtual environment enabling the co-visualization of historical photos into a contemporary 3D scene. We address the topic of user interaction considering the potential volume of the input data. Our methodology is based on design guidelines that rely on visual perception techniques to ease visual complexity and improve saliency on specific cues. The designs are additionally implemented following an image-based rendering approach and evaluated in a group of users. Overall, these propositions may be a notable addition to creating innovative ways to visualize and discover historical images in a virtual geographic environment.

Fully Automated Pose Estimation of Historical Images in the Context of 4D Geographic Information Systems Utilizing Machine Learning Methods

The idea of virtual time machines in digital environments like hand-held virtual reality or four-dimensional (4D) geographic information systems requires an accurate positioning and orientation of urban historical images. The browsing of large repositories to retrieve historical images and their subsequent precise pose estimation is still a manual and time-consuming process in the field of Cultural Heritage. This contribution presents an end-to-end pipeline from finding relevant images with utilization of content-based image retrieval to photogrammetric pose estimation of large historical terrestrial image datasets. Image retrieval as well as pose estimation are challenging tasks and are subjects of current research. Thereby, research has a strong focus on contemporary images but the methods are not considered for a use on historical image material. The first part of the pipeline comprises the precise selection of many relevant historical images based on a few example images (so called query images) by using content-based image retrieval. Therefore, two different retrieval approaches based on convolutional neural networks (CNN) are tested, evaluated, and compared with conventional metadata search in repositories. Results show that image retrieval approaches outperform the metadata search and are a valuable strategy for finding images of interest. The second part of the pipeline uses techniques of photogrammetry to derive the camera position and orientation of the historical images identified by the image retrieval. Multiple feature matching methods are used on four different datasets, the scene is reconstructed in the Structure-from-Motion software COLMAP, and all experiments are evaluated on a newly generated historical benchmark dataset. A large number of oriented images, as well as low error measures for most of the datasets, show that the workflow can be successfully applied. Finally, the combination of a CNN-based image retrieval and the feature matching methods SuperGlue and DISK show very promising results to realize a fully automated workflow. Such an automated workflow of selection and pose estimation of historical terrestrial images enables the creation of large-scale 4D models.

An overview of the National COVID-19 Chest Imaging Database: data quality and cohort analysis

Background The National COVID-19 Chest Imaging Database (NCCID) is a centralized database containing mainly chest X-rays and computed tomography scans from patients across the UK. The objective of the initiative is to support a better understanding of the coronavirus SARS-CoV-2 disease (COVID-19) and the development of machine learning technologies that will improve care for patients hospitalized with a severe COVID-19 infection. This article introduces the training dataset, including a snapshot analysis covering the completeness of clinical data, and availability of image data for the various use-cases (diagnosis, prognosis, longitudinal risk). An additional cohort analysis measures how well the NCCID represents the wider COVID-19–affected UK population in terms of geographic, demographic, and temporal coverage. Findings The NCCID offers high-quality DICOM images acquired across a variety of imaging machinery; multiple time points including historical images are available for a subset of patients. This volume and variety make the database well suited to development of diagnostic/prognostic models for COVID-associated respiratory conditions. Historical images and clinical data may aid long-term risk stratification, particularly as availability of comorbidity data increases through linkage to other resources. The cohort analysis revealed good alignment to general UK COVID-19 statistics for some categories, e.g., sex, whilst identifying areas for improvements to data collection methods, particularly geographic coverage. Conclusion The NCCID is a growing resource that provides researchers with a large, high-quality database that can be leveraged both to support the response to the COVID-19 pandemic and as a test bed for building clinically viable medical imaging models.

Integration of Historical Images–Reconstructions into the Infrastructure of the Recreated Fortification Complexes (On the Example of the Abalak Field Festival)

Цель исследования – выявление потенциала интеграции исторических образов-реконструкций в инфраструктуру воссозданного объекта оборонного зодчества на примере фестиваля «Абалакское поле» (Тюменская область). Привлечены фактографические данные и фотоматериалы,выявленные в сети Интернет, результаты научных изысканий историков архитектуры, культурологов и искусствоведов, связанные с проблематикой исторической реконструкции объектов архитектуры и архитектурно-исторической среды. Исследована инфраструктура комплекса «Абалакская крепость», выделены ее локусы (компоненты), определены исторические образы-реконструкции, связанные с каждым из них, выявлены пути интеграции этих образов в архитектурную среду воссозданной крепости. Установлено, что локусы инфраструктуры, формы историко-культурной реконструкции, использованные в них, и образы-реконструкции, интегрированные в фестивальное действо, образуют три сферы, от оптимального сочетания которых зависит успех фестиваля реконструкции. The aim of the study is to identify the potential for the integration of historical images–reconstructions into the infrastructure of a recreated object of defense architecture using the example of the Abalak Field Festival (Tyumen Oblast). This festival has been held in the vicinity of Tobolsk since 2009 and annually attracts up to 20 thousand participants fond of historical reconstruction. The author involved factual data and photographic materials found on the Internet, the results of scientific research by architectural historians, cultural experts and art historians related to the problems of historical reconstruction of architectural objects and the architectural and historical environment. The use of an integrative-infrastructural approach has been substantiated; it involves the application of methods of visual-figurative analysis of photo and video documents. Through this analysis, associative information is extracted from visual sources, which then serves as one of the resources for obtaining innovative theoretical results. The study of the infrastructure of the Abalak Fortress complex made it possible to identify its loci, that is, the components of the infrastructure of the reconstructed fortification complex focused on individual fragments of historical memory and its actualization. On the basis of these loci, by the method of visual-figurative analysis, the historical images–reconstructions associated with them were determined and the ways of their integration into the architectural environment of the reconstructed fortress were identified. It has been established that the infrastructure loci, the forms of historical and cultural reconstruction distributed over them, and the images–reconstructions included in the festival action form three spheres; and the success of the festival of historical reconstruction depends on their optimal combination. The following components of the infrastructure of the Abalak Fortress have been identified: a space for guard; a space for dwelling, celebrations and feasts; a tiltyard (place for fights); a space for fairs, craft workshops; a space for recreation and entertainment. The algorithm of the sequence of images–reconstructions associated with the Abalak Fortress is determined. These images, integrated into six loci, generally characterize the architectural ensemble of the fortress, give visitors, tourists, participants in festivals of historical reconstruction a visual and figurative understanding of the past in its various manifestations, help to assimilate folk traditions and recreate them in various forms in any historical territories.

DATA FUSION OF HISTORICAL PHOTOGRAPHS WITH MODERN 3D DATA FOR AN ARCHAEOLOGICAL EXCAVATION – CONCEPT AND FIRST RESULTS

Through the destruction of war, most of the documents of an archaeological excavation from 1934 – 1939 of a megalithic tomb in north-west Germany have been destroyed irretrievably. Fortunately, more than 500 historical pictures have been preserved, which visually document the excavation situation at that time. Parts of the image collection are preserved on fragile glass plates that are difficult to preserve and have to be digitised urgendly. A method for digitising these glass plates will be presented first. With the help of the digitised historical images, the excavation situation at that time shall be reconstructed. Since a reconstruction based only on the historical images is not possible, the current state of the megalithic tombs has been recorded with modern measuring technology and a 3D model has been calculated. The aim is to fuse the historical images with the modern 3D model. For this purpose, different possibilities of linking the data are presented. As first results, point clouds calculated by Structure from Motion and the orientation of historical images in relation to the modern 3D model using direct linear transformation are shown. The hybrid model of historical and modern data will be used for archaeological interpretations of the excavation.

Geopolitical culture

The monograph examines geopolitics from the point of view of filling its content with the meanings of geopolitical culture, in which not only geographical, but also historical images occupy a prominent place, and linguistic constructions allow us to attach a symbolic meaning to established concepts. Geopolitical culture, like any other, acts as a tool for processing consciousness and transforming space. The space itself, from the perspective of studies of geopolitical culture, turns into a multidimensional model that simultaneously combines real objects and elements related to the world of geopolitical imagination. It is intended for specialists in geopolitics, history and theory of international relations, sociology and cultural studies. It will also be of interest to a wide range of readers.

Below the Mosul Dam Lake. Geomorphological reconstruction of historical fluvial pattern of the Tigris River

<p>Located along the Tigris River in the Kurdistan Region of Iraq (KRI), the Mosul Dam reservoir is the second biggest dam of the Near East, and represents an important water storage for local human activities. The Dam was built between 1981 and 1988 north of the village of Eski Mosul; along this part of the Tigris River several archaeological sites were inundated. Analysis of historical images derived from Declassified Corona satellite acquired between December 1967 and August 1968 reveals seasonal changes of the Tigris riverbed, shifting across the hydrological year from meandering to anastomosing. The geomorphological mapping was carried out on the December 1967 and. in August 1968, Corona images were taken, in order to estimate the modification of several fluvial geomorphological elements such as floodplain and point, middle and longitudinal bars. Here, such evidence is compared with Landsat data collected between the 1990ies and today, in order to detect the first phases of filling of the basin and the control of inherited Tigris channel belt over the reservoir. Moreover, we also noticed an influence of the ancient Tigris course on is recent insertion into the lake. Our work permitted to reconstruct the ancient fluvial landscape below the Mosul Dam Lake, and its evolution in response to seasonal variation of the discharge.</p>