Use of a high resolution magnetic map by a short distance migrant, the alpine newt (Ichthyosaura alpestris)?

Newts can use spatial variation in the magnetic field (MF) to derive geographic position, but it is unclear how they detect the ‘spatial signal’ which, over the distances that newts move in a day, is an order of magnitude lower than temporal variation in the MF. Newts take map readings using their light-dependent magnetic compass to align a magnetite-based ‘map detector’ relative to the MF. Time-of-day, location, and light exposure (required by the magnetic compass) were varied to determine when newts obtain map information. Newts were displaced from breeding ponds without access to route-based cues to sites where they were held and/or tested under diffuse natural illumination. We found that: (1) newts held overnight at the testing site under diffuse illumination exhibited accurate homing orientation, but not if transported to the testing site on the day of testing, (2) newts held overnight under diffuse lighting at a ‘false testing site’ and then tested at a site located in a different direction from their home pond, oriented in the home direction from the holding site, not from the site where they were tested, (3) newts held overnight in total darkness (except for light exposure for specific periods) only exhibited homing orientation the following day if exposed to diffuse illumination during the preceding evening twilight in the ambient MF. These findings demonstrate that, to determine the home direction, newts require access to light and the ambient MF during evening twilight when temporal variation in the MF is minimal.

Hash-Based Hierarchical Caching and Layered Filtering for Interactive Previews in Global Illumination Rendering

Modern Monte-Carlo-based rendering systems still suffer from the computational complexity involved in the generation of noise-free images, making it challenging to synthesize interactive previews. We present a framework suited for rendering such previews of static scenes using a caching technique that builds upon a linkless octree. Our approach allows for memory-efficient storage and constant-time lookup to cache diffuse illumination at multiple hitpoints along the traced paths. Non-diffuse surfaces are dealt with in a hybrid way in order to reconstruct view-dependent illumination while maintaining interactive frame rates. By evaluating the visual fidelity against ground truth sequences and by benchmarking, we show that our approach compares well to low-noise path-traced results, but with a greatly reduced computational complexity, allowing for interactive frame rates. This way, our caching technique provides a useful tool for global illumination previews and multi-view rendering.

Hash-Based Hierarchical Caching and Layered Filtering for Interactive Previews in Global Illumination Rendering

Modern Monte-Carlo-based rendering systems still suffer from the computational complexity involved in the generation of noise-free images, making it challenging to synthesize interactive previews. We present a framework suited for rendering such previews of static scenes using a caching technique that builds upon a linkless octree. Our approach allows for memory-efficient storage and constant-time lookup to cache diffuse illumination at multiple hitpoints along the traced paths. Non-diffuse surfaces are dealt with in a hybrid way in order to reconstruct view-dependent illumination while maintaining interactive frame rates. By evaluating the visual fidelity against ground truth sequences and by benchmarking, we show that our approach compares well to low-noise path traced results, but with a greatly reduced computational complexity allowing for interactive frame rates. This way, our caching technique provides a useful tool for global illumination previews and multi-view rendering.

Diffuse retro-reflective imaging for improved video tracking of mosquitoes at human baited bednets

Robust imaging techniques for tracking insects have been essential tools in numerous laboratory and field studies on pests, beneficial insects and model systems. Recent innovations in optical imaging systems and associated signal processing have enabled detailed characterisation of nocturnal mosquito behaviour around bednets and improvements in bednet design, a global essential for protecting populations against malaria. Nonetheless, there remain challenges around ease of use for large scale in situ recordings and extracting data reliably in the critical areas of the bednet where the optical signal is attenuated. Here we introduce a retro-reflective screen at the back of the measurement volume, which can simultaneously provide diffuse illumination, and remove optical alignment issues whilst requiring only one-sided access to the measurement space. The illumination becomes significantly more uniform, although, noise removal algorithms are needed to reduce the effects of shot noise particularly across low intensity bednet regions. By systematically introducing mosquitoes in front and behind the bednet in lab experiments we are able to demonstrate robust tracking in these challenging areas. Overall, the retro-reflective imaging setup delivers mosquito segmentation rates in excess of 90% compared to less than 70% with back-lit systems.

Image Classification for Automated Image Cross-Correlation Applications in the Geosciences

In Earth Science, image cross-correlation (ICC) can be used to identify the evolution of active processes. However, this technology can be ineffective, because it is sometimes difficult to visualize certain phenomena, and surface roughness can cause shadows. In such instances, manual image selection is required to select images that are suitably illuminated, and in which visibility is adequate. This impedes the development of an autonomous system applied to ICC in monitoring applications. In this paper, the uncertainty introduced by the presence of shadows is quantitatively analysed, and a method suitable for ICC applications is proposed: The method automatically selects images, and is based on a supervised classification of images using the support vector machine. According to visual and illumination conditions, the images are divided into three classes: (i) No visibility, (ii) direct illumination and (iii) diffuse illumination. Images belonging to the diffuse illumination class are used in cross-correlation processing. Finally, an operative procedure is presented for applying the automated ICC processing chain in geoscience monitoring applications.