Abstract. We introduce a real-time stereo matching technique based on a reformulation of Yoon and Kweon's adaptive support weights algorithm [1]. Our implementation uses the bilateral grid to achieve a speedup of 200× compared to a straightforward full-kernel GPU implementation, making it the fastest technique on the Middlebury website. We introduce a colour component into our greyscale approach to recover precision and increase discriminability. Using our implementation, we speed up spatialdepth superresolution 100×. We further present a spatiotemporal stereo matching approach based on our technique that incorporates temporal evidence in real time (>14 fps). Our technique visibly reduces flickering and outperforms per-frame approaches in the presence of image noise. We have created five synthetic stereo videos, with ground truth disparity maps, to quantitatively evaluate depth estimation from stereo video. Source code and datasets are available on our project website 3 .

British sheep farmers were invited to complete a questionnaire about the impact of Schmallenberg virus (SBV) on animal health, welfare and their own emotional wellbeing during the 2011–2012 lambing season, through Defra and Farming Industry websites, letters to farmers who had requested SBV laboratory tests and advertisement at Sheep 2012. The 494 responders included SBV confirmed (positive by RT-PCR) (n=76), SBV suspected by farmer (n=140) or SBV not suspected (n=278). Percentage of barren ewes was similar across SBV groups, however, lamb and ewe losses were higher on responder farms where SBV was confirmed or suspected. The median percentages of all lambs born (and lambs born deformed ) that died within one week of birth was 10.4 per cent (5.5 per cent), 7.0 per cent (2.9 per cent) and 5.3 per cent (0 per cent), respectively, on SBV confirmed, suspected and not suspected farms (P<0.001). Eight to 16 per cent of SBV confirmed or suspected farms reported lamb mortality of ≥40 per cent. Farmer perceived impact was greater where SBV was confirmed or suspected (P<0.001): 25 per cent reported a high impact on emotional wellbeing (4 per cent of SBV not suspected), 13 per cent reported a high impact on flock welfare and financial performance and 6 per cent were less likely to farm sheep next year because of SBV (<2 per cent in SBV not suspected). Overall, SBV impact has been large relative to reported sheep loss.

No abstract

Airborne volcanic ash particles are a known hazard to aviation. Currently, there are no means available to detect ash in flight as the particles are too fine (radii < 30 μm) for on-board radar detection and, even in good visibility, ash clouds are difficult or impossible to detect by eye. The economic cost and societal impact of the April/May 2010 Icelandic eruption of Eyjafjallajökull generated renewed interest in finding ways to identify airborne volcanic ash in order to keep airspace open and avoid aircraft groundings. We have designed and built a bi-spectral, fast-sampling, uncooled infrared camera device (AVOID) to examine its ability to detect volcanic ash from commercial jet aircraft at distances of more than 50 km ahead. Here we report results of an experiment conducted over the Atlantic Ocean, off the coast of France, confirming the ability of the device to detect and quantify volcanic ash in an artificial ash cloud created by dispersal of volcanic ash from a second aircraft. A third aircraft was used to measure the ash in situ using optical particle counters. The cloud was composed of very fine ash (mean radii ~10 μm) collected from Iceland immediately after the Eyjafjallajökull eruption and had a vertical thickness of ~200 m, a width of ~2 km and length of between 2 and 12 km. Concentrations of ~200 μg m−3 were identified by AVOID at distances from ~20 km to ~70 km. For the first time, airborne remote detection of volcanic ash has been successfully demonstrated from a long-range flight test aircraft.

We introduce a novel computational model for objectively assessing the visual comfort of stereoscopic 3D imagery. Our model integrates research in visual perception with tools from stereo computer vision to quantify the degree of stereo coherence between both stereo half-images. We show that the coherence scores computed by our model strongly correlate with human comfort ratings using a perceptual study of 20 participants rating 80 images each. Based on our experiments, we further propose a taxonomy of stereo coherence issues which affect viewing comfort, and propose a set of computational tools that extend our model to identify and localise stereo coherence issues from stereoscopic 3D images.