A feasibility assessment of emergent technology for use in Antarctica

Both the Antarctic Treaty 1959, and Madrid Protocol 1991, set as one of their leading principles “The protection of the environment”. As such operations in Antarctica, and the operators behind them, should constantly be seeking more efficient and renewable ways of achieving processes. COMNAP recently undertook the ARC project which sought out to find challenges in regards to future scientific endeavours. This project will look at these challenges, and highlight potential emergent technology that may help to confront these challenges, as well as better achieve the purpose of environmental protection, under the Antarctic Treaty

SceneNet: Understanding Real World Indoor Scenes With Synthetic Data

Scene understanding is a prerequisite to many high level tasks for any automated intelligent machine operating in real world environments. Recent attempts with supervised learning have shown promise in this direction but also highlighted the need for enormous quantity of supervised data --- performance increases in proportion to the amount of data used. However, this quickly becomes prohibitive when considering the manual labour needed to collect such data. In this work, we focus our attention on depth based semantic per-pixel labelling as a scene understanding problem and show the potential of computer graphics to generate virtually unlimited labelled data from synthetic 3D scenes. By carefully synthesizing training data with appropriate noise models we show comparable performance to state-of-the-art RGBD systems on NYUv2 dataset despite using only depth data as input and set a benchmark on depth-based segmentation on SUN RGB-D dataset. Additionally, we offer a route to generating synthesized frame or video data, and understanding of different factors influencing performance gains

SynthCam3D: Semantic Understanding With Synthetic Indoor Scenes

We are interested in automatic scene understanding from geometric cues. To this end, we aim to bring semantic segmentation in the loop of real-time reconstruction. Our semantic segmentation is built on a deep autoencoder stack trained exclusively on synthetic depth data generated from our novel 3D scene library, SynthCam3D. Importantly, our network is able to segment real world scenes without any noise modelling. We present encouraging preliminary results

Visual change detection on tunnel linings

We describe an automated system for detecting, localising, clustering and ranking visual changes on tunnel surfaces. The system is designed to provide assistance to expert human inspectors carrying out structural health monitoring and maintenance on ageing tunnel networks. A three-dimensional tunnel surface model is first recovered from a set of reference images using Structure from Motion techniques. New images are localised accurately within the model and changes are detected versus the reference images and model geometry. We formulate the problem of detecting changes probabilistically and evaluate the use of different feature maps and a novel geometric prior to achieve invariance to noise and nuisance sources such as parallax and lighting changes. A clustering and ranking method is proposed which efficiently presents detected changes and further improves the inspection efficiency. System performance is assessed on a real data set collected using a low-cost prototype capture device and labelled with ground truth. Results demonstrate that our system is a step towards higher frequency visual inspection at a reduced cost.The authors gratefully acknowledge the support by Toshiba Research Europe.This is the accepted manuscript. The final publication is available at Springer via http://dx.doi.org/10.1007/s00138-014-0648-8

Assessing the Antarctic lithodidae (King crab) hypothesis: invasion or endurance?

Rising sea temperature, as a result of anthropogenic climate change, has contributed to dynamic ecological changes across the globe. As a result the previously isolated ecosystem of Antarctica will likely soon be at risk of invasive species migration. Currently the Antarctic continental shelf is unique in its lack of decapods, though the recent discovery of dense populations of Lithodid crabs in Antarctic waters, has caused concerns of a possible invasion event already occurring. Though it is argued that the recent discovery is a result of poor historical fossil records and inadequate sampling methods, the potential ecological impact of increased Lithodid crab distribution on the Antarctic shelf benthos, is likely to be severe

SynthCam3D: Semantic Understanding With Synthetic Indoor Scenes

We are interested in automatic scene understanding from geometric cues. To this end, we aim to bring semantic segmentation in the loop of real-time reconstruction. Our semantic segmentation is built on a deep autoencoder stack trained exclusively on synthetic depth data generated from our novel 3D scene library, SynthCam3D. Importantly, our network is able to segment real world scenes without any noise modelling. We present encouraging preliminary results