Digital 3D Technologies for Humanities Research and Education: An Overview

Digital 3D modelling and visualization technologies have been widely applied to support research in the humanities since the 1980s. Since technological backgrounds, project opportunities, and methodological considerations for application are widely discussed in the literature, one of the next tasks is to validate these techniques within a wider scientific community and establish them in the culture of academic disciplines. This article resulted from a postdoctoral thesis and is intended to provide a comprehensive overview on the use of digital 3D technologies in the humanities with regards to (1) scenarios, user communities, and epistemic challenges; (2) technologies, UX design, and workflows; and (3) framework conditions as legislation, infrastructures, and teaching programs. Although the results are of relevance for 3D modelling in all humanities disciplines, the focus of our studies is on modelling of past architectural and cultural landscape objects via interpretative 3D reconstruction methods

Recognizing Characters in Art History Using Deep Learning

In the field of Art History, images of artworks and their contexts are core to understanding the underlying semantic information. However, the highly complex and sophisticated representation of these artworks makes it difficult, even for the experts, to analyze the scene. From the computer vision perspective, the task of analyzing such artworks can be divided into sub-problems by taking a bottom-up approach. In this paper, we focus on the problem of recognizing the characters in Art History. From the iconography of $Annunciation$ $of$ $the$ $Lord$ (Figure 1), we consider the representation of the main protagonists, $Mary$ and $Gabriel$, across different artworks and styles. We investigate and present the findings of training a character classifier on features extracted from their face images. The limitations of this method, and the inherent ambiguity in the representation of $Gabriel$, motivated us to consider their bodies (a bigger context) to analyze in order to recognize the characters. Convolutional Neural Networks (CNN) trained on the bodies of $Mary$ and $Gabriel$ are able to learn person related features and ultimately improve the performance of character recognition. We introduce a new technique that generates more data with similar styles, effectively creating data in the similar domain. We present experiments and analysis on three different models and show that the model trained on domain related data gives the best performance for recognizing character. Additionally, we analyze the localized image regions for the network predictions. Code is open-sourced and available at https://github.com/prathmeshrmadhu/recognize_characters_art_history and the link to the published peer-reviewed article is https://dl.acm.org/citation.cfm?id=3357242

Deep Descriptor Learning with Auxiliary Classification Loss for Retrieving Images of Silk Fabrics in the Context of Preserving European Silk Heritage

With the growing number of digitally available collections consisting of images depicting relevant objects from the past in relation with descriptive annotations, the need for suitable information retrieval techniques is becoming increasingly important to support historians in their work. In this context, we address the problem of image retrieval for searching records in a database of silk fabrics. The descriptors, used as an index to the database, are learned by a convolutional neural network, exploiting the available annotations to automatically generate training data. Descriptor learning is combined with auxiliary classification loss with the aim of supporting the clustering in the descriptor space with respect to the properties of the depicted silk objects, such as the place or time of origin. We evaluate our approach on a dataset of fabric images in a kNN-classification, showing promising results with respect to the ability of the descriptors to represent semantic properties of silk fabrics; integrating the auxiliary loss improves the overall accuracy by 2.7% and the average F1 score by 5.6%. It can be observed that the largest improvements can be obtained for variables with imbalanced class distributions. An evaluation on the WikiArt dataset demonstrates the transferability of our approach to other digital collection

A Holmes and Doyle Bibliography, Volume 9: All Formats—Combined Alphabetical Listing

This bibliography is a work in progress. It attempts to update Ronald B. De Waal’s comprehensive bibliography, The Universal Sherlock Holmes, but does not claim to be exhaustive in content. New works are continually discovered and added to this bibliography. Readers and researchers are invited to suggest additional content. This volume contains all listings in all formats, arranged alphabetically by author or main entry. In other words, it combines the listings from Volume 1 (Monograph and Serial Titles), Volume 3 (Periodical Articles), and Volume 7 (Audio/Visual Materials) into a comprehensive bibliography. (There may be additional materials included in this list, e.g. duplicate items and items not yet fully edited.) As in the other volumes, coverage of this material begins around 1994, the final year covered by De Waal's bibliography, but may not yet be totally up-to-date (given the ongoing nature of this bibliography). It is hoped that other titles will be added at a later date. At present, this bibliography includes 12,594 items

A Holmes and Doyle Bibliography, Volume 6: Periodical Articles, Subject Listing, By De Waal Category

This bibliography is a work in progress. It attempts to update Ronald B. De Waal’s comprehensive bibliography, The Universal Sherlock Holmes, but does not claim to be exhaustive in content. New works are continually discovered and added to this bibliography. Readers and researchers are invited to suggest additional content. Volume 6 presents the periodical literature arranged by subject categories (as originally devised for the De Waal bibliography and slightly modified here)