Visual Impairment and Falls Risk in the Elderly

Vision is highly important for balance and gait and visual impairments are significantly associated with locomotion problems and falls in older people. There is now a large body of research linking falls and fall-related injuries with visual problems, some of which are easily remedied by surgery or refractive correction. However there is also evidence that the kind of refractive correction provided (in terms of single-vision or multifocal correction) can also have an effect on fall risk. This chapter provides an overview of the major findings in this area.

East Asian audio-visual collaboration and the global expansion of Chinese media

In recent years, many of the world’s leading media producers, screenwriters, technicians and investors, particularly those in the Asia-Pacific region, have been drawn to work in the People's Republic of China (hereafter China or Mainland China). Media projects with a lighter commercial entertainment feel – compared with the heavy propaganda-oriented content of the past – have multiplied, thanks to the Chinese state’s newfound willingness to consider collaboration with foreign partners. This is no more evident than in film. Despite their long-standing reputation for rigorous censorship, state policymakers are now encouraging Chinese media entrepreneurs to generate fresh ideas and to develop products that will revitalise the stagnant domestic production sector. It is hoped that an increase in both the quality and quantity of domestic feature films, stimulated by an infusion of creativity and cutting-edge technology from outside the country, will help reverse China’s ‘cultural trade deficit’ (wenhua maoyi chizi) (Keane 2007).

Instagrammatics and digital methods: Studying visual social media, from selfies and GIFs to memes and emoji

Visual content is a critical component of everyday social media, on platforms explicitly framed around the visual (Instagram and Vine), on those offering a mix of text and images in myriad forms (Facebook, Twitter, and Tumblr), and in apps and profiles where visual presentation and provision of information are important considerations. However, despite being so prominent in forms such as selfies, looping media, infographics, memes, online videos, and more, sociocultural research into the visual as a central component of online communication has lagged behind the analysis of popular, predominantly text-driven social media. This paper underlines the increasing importance of visual elements to digital, social, and mobile media within everyday life, addressing the significant research gap in methods for tracking, analysing, and understanding visual social media as both image-based and intertextual content. In this paper, we build on our previous methodological considerations of Instagram in isolation to examine further questions, challenges, and benefits of studying visual social media more broadly, including methodological and ethical considerations. Our discussion is intended as a rallying cry and provocation for further research into visual (and textual and mixed) social media content, practices, and cultures, mindful of both the specificities of each form, but also, and importantly, the ongoing dialogues and interrelations between them as communication forms.

Deeper and wider fully convolutional network coupled with conditional random fields for scene labeling

Deep convolutional neural networks (DCNNs) have been employed in many computer vision tasks with great success due to their robustness in feature learning. One of the advantages of DCNNs is their representation robustness to object locations, which is useful for object recognition tasks. However, this also discards spatial information, which is useful when dealing with topological information of the image (e.g. scene labeling, face recognition). In this paper, we propose a deeper and wider network architecture to tackle the scene labeling task. The depth is achieved by incorporating predictions from multiple early layers of the DCNN. The width is achieved by combining multiple outputs of the network. We then further refine the parsing task by adopting graphical models (GMs) as a post-processing step to incorporate spatial and contextual information into the network. The new strategy for a deeper, wider convolutional network coupled with graphical models has shown promising results on the PASCAL-Context dataset.

'Ladd traps' as a visual trap for male and female Queensland fruit fly, Bactrocera tryoni (Diptera: Tephritidae)

Bactrocera tryoni (Froggatt) is Australia's major horticultural insect pest, yet monitoring females remains logistically difficult. We trialled the ‘Ladd trap’ as a potential female surveillance or monitoring tool. This trap design is used to trap and monitor fruit flies in countries other (e.g. USA) than Australia. The Ladd trap consists of a flat yellow panel (a traditional ‘sticky trap’), with a three dimensional red sphere (= a fruit mimic) attached in the middle. We confirmed, in field-cage trials, that the combination of yellow panel and red sphere was more attractive to B. tryoni than the two components in isolation. In a second set of field-cage trials, we showed that it was the red-yellow contrast, rather than the three dimensional effect, which was responsible for the trap's effectiveness, with B. tryoni equally attracted to a Ladd trap as to a two-dimensional yellow panel with a circular red centre. The sex ratio of catches was approximately even in the field-cage trials. In field trials, we tested the traditional red-sphere Ladd trap against traps for which the sphere was painted blue, black or yellow. The colour of sphere did not significantly influence trap efficiency in these trials, despite the fact the yellow-panel/yellow-sphere presented no colour contrast to the flies. In 6 weeks of field trials, over 1500 flies were caught, almost exactly two-thirds of them being females. Overall, flies were more likely to be caught on the yellow panel than the sphere; but, for the commercial Ladd trap, proportionally more females were caught on the red sphere versus the yellow panel than would be predicted based on relative surface area of each component, a result also seen the field-cage trial. We determined that no modification of the trap was more effective than the commercially available Ladd trap and so consider that product suitable for more extensive field testing as a B. tryoni research and monitoring tool.

Visual performance at passive level crossings with long sighting distances

The third edition of the Australian Standard AS1742 Manual of Uniform Traffic Control Devices Part 7 provides a method of calculating the sighting distance required to safely proceed at passive level crossings based on the physics of moving vehicles. This required distance becomes greater with higher line speeds and slower, heavier vehicles so that it may return quite a long sighting distance. However, at such distances, there are also concerns around whether drivers would be able to reliably identify a train in order to make an informed decision regarding whether it would be safe to proceed across the level crossing. In order to determine whether drivers are able to make reliable judgements to proceed in these circumstances, this study assessed the distance at which a train first becomes identifiable to a driver as well as their, ability to detect the movement of the train. A site was selected in Victoria, and 36 participants with good visual acuity observed 4 trains in the 100-140 km/h range. While most participants could detect the train from a very long distance (2.2 km on average), they could only detect that the train was moving at much shorter distances (1.3 km on average). Large variability was observed between participants, with 4 participants consistently detecting trains later than other participants. Participants tended to improve in their capacity to detect the presence of the train with practice, but a similar trend was not observed for detection of the movement of the train. Participants were consistently poor at accurately judging the approach speed of trains, with large underestimations at all investigated distances.