Digital representation of historical globes : methods to make 3D and pseudo-3D models of sixteenth century Mercator globes

In this paper, the construction of digital representations of a terrestrial and celestial globe will be discussed. Virtual digital (3D) models play an important role in recent research and publications on cultural heritage. The globes discussed in this paper were made by Gerardus Mercator (1512-1594) in 1541 and 1551. Four techniques for the digital representation are discussed and analysed, all using high-resolution photographs of the globes. These photographs were taken under studio conditions in order to get equal lighting and to avoid unwanted light spots. These lighting conditions are important, since the globes have a highly reflective varnish covering. Processing these images using structure from motion, georeferencing of separate scenes and the combination of the photographs with terrestrial laser scanning data results in true 3D representations of the globes. Besides, pseudo-3D models of these globes were generated using dynamic imaging, which is an extensively used technique for visualisations over the Internet. The four techniques and the consequent results are compared on geometric and radiometric quality, with a special focus on their usefulness for distribution and visualisation during an exhibition in honour of the five hundredth birthday of Gerardus Mercator

Reflectance Transformation Imaging (RTI) System for Ancient Documentary Artefacts

This tutorial summarises our uses of reflectance transformation imaging in archaeological contexts. It introduces the UK AHRC funded project reflectance Transformation Imaging for Anciant Documentary Artefacts and demonstrates imaging methodologies

DeepKey: Towards End-to-End Physical Key Replication From a Single Photograph

This paper describes DeepKey, an end-to-end deep neural architecture capable of taking a digital RGB image of an 'everyday' scene containing a pin tumbler key (e.g. lying on a table or carpet) and fully automatically inferring a printable 3D key model. We report on the key detection performance and describe how candidates can be transformed into physical prints. We show an example opening a real-world lock. Our system is described in detail, providing a breakdown of all components including key detection, pose normalisation, bitting segmentation and 3D model inference. We provide an in-depth evaluation and conclude by reflecting on limitations, applications, potential security risks and societal impact. We contribute the DeepKey Datasets of 5, 300+ images covering a few test keys with bounding boxes, pose and unaligned mask data.Comment: 14 pages, 12 figure

Indirect Match Highlights Detection with Deep Convolutional Neural Networks

Highlights in a sport video are usually referred as actions that stimulate excitement or attract attention of the audience. A big effort is spent in designing techniques which find automatically highlights, in order to automatize the otherwise manual editing process. Most of the state-of-the-art approaches try to solve the problem by training a classifier using the information extracted on the tv-like framing of players playing on the game pitch, learning to detect game actions which are labeled by human observers according to their perception of highlight. Obviously, this is a long and expensive work. In this paper, we reverse the paradigm: instead of looking at the gameplay, inferring what could be exciting for the audience, we directly analyze the audience behavior, which we assume is triggered by events happening during the game. We apply deep 3D Convolutional Neural Network (3D-CNN) to extract visual features from cropped video recordings of the supporters that are attending the event. Outputs of the crops belonging to the same frame are then accumulated to produce a value indicating the Highlight Likelihood (HL) which is then used to discriminate between positive (i.e. when a highlight occurs) and negative samples (i.e. standard play or time-outs). Experimental results on a public dataset of ice-hockey matches demonstrate the effectiveness of our method and promote further research in this new exciting direction.Comment: "Social Signal Processing and Beyond" workshop, in conjunction with ICIAP 201