Impact of region-of-interest video coding on perceived quality in mobile video

Effective streaming of video can be achieved by providing more bits to the most important region in the frame at the cost of reduced bits in the less important regions. This strategy can be beneficial for delivering high quality videos in mobile devices, especially when the availability of bandwidth is usually low and limited. While the state-of-the-art video codecs such as H.264 may have been optimised for perceived quality, it is hypothesised that users will give more attention to interesting region/object when watching videos. Therefore, giving a higher quality to region of interest (ROI)while reducing quality of other areas may result in improving the overall perceived quality without necessarily increasing the bitrate. In this paper, the impact of ROI-based encoded video on perceived quality is investigated by conducting a user study for varous target bitrates. The results from the user study demonstrate that ROI-based video coding has superior perceived quality compared to normal encoded video at the same bitrate in the lower bitrate range.

Measuring reef complexity and rugosity from monocular video bathymetric reconstruction

Topographic structural complexity of a reef is highly correlated to coral growth rates, coral cover and overall levels of biodiversity, and is therefore integral in determining ecological processes. Modeling these processes commonly includes measures of rugosity obtained from a wide range of different survey techniques that often fail to capture rugosity at different spatial scales. Here we show that accurate estimates of rugosity can be obtained from video footage captured using underwater video cameras (i.e., monocular video). To demonstrate the accuracy of our method, we compared the results to in situ measurements of a 2m x 20m area of forereef from Glovers Reef atoll in Belize. Sequential pairs of images were used to compute fine scale bathymetric reconstructions of the reef substrate from which precise measurements of rugosity and reef topographic structural complexity can be derived across multiple spatial scales. To achieve accurate bathymetric reconstructions from uncalibrated monocular video, the position of the camera for each image in the video sequence and the intrinsic parameters (e.g., focal length) must be computed simultaneously. We show that these parameters can be often determined when the data exhibits parallax-type motion, and that rugosity and reef complexity can be accurately computed from existing video sequences taken from any type of underwater camera from any reef habitat or location. This technique provides an infinite array of possibilities for future coral reef research by providing a cost-effective and automated method of determining structural complexity and rugosity in both new and historical video surveys of coral reefs.

A review of current statistical methodologies for in storage sampling and surveillance in the grains industry

Effective, statistically robust sampling and surveillance strategies form an integral component of large agricultural industries such as the grains industry. Intensive in-storage sampling is essential for pest detection, Integrated Pest Management (IPM), to determine grain quality and to satisfy importing nation’s biosecurity concerns, while surveillance over broad geographic regions ensures that biosecurity risks can be excluded, monitored, eradicated or contained within an area. In the grains industry, a number of qualitative and quantitative methodologies for surveillance and in-storage sampling have been considered. Primarily, research has focussed on developing statistical methodologies for in storage sampling strategies concentrating on detection of pest insects within a grain bulk, however, the need for effective and statistically defensible surveillance strategies has also been recognised. Interestingly, although surveillance and in storage sampling have typically been considered independently, many techniques and concepts are common between the two fields of research. This review aims to consider the development of statistically based in storage sampling and surveillance strategies and to identify methods that may be useful for both surveillance and in storage sampling. We discuss the utility of new quantitative and qualitative approaches, such as Bayesian statistics, fault trees and more traditional probabilistic methods and show how these methods may be used in both surveillance and in storage sampling systems.

Towards spontaneous facial expression recognition in real-world video

Facial expression is an important channel of human social communication. Facial expression recognition (FER) aims to perceive and understand emotional states of humans based on information in the face. Building robust and high performance FER systems that can work in real-world video is still a challenging task, due to the various unpredictable facial variations and complicated exterior environmental conditions, as well as the difficulty of choosing a suitable type of feature descriptor for extracting discriminative facial information. Facial variations caused by factors such as pose, age, gender, race and occlusion, can exert profound influence on the robustness, while a suitable feature descriptor largely determines the performance. Most present attention on FER has been paid to addressing variations in pose and illumination. No approach has been reported on handling face localization errors and relatively few on overcoming facial occlusions, although the significant impact of these two variations on the performance has been proved and highlighted in many previous studies. Many texture and geometric features have been previously proposed for FER. However, few comparison studies have been conducted to explore the performance differences between different features and examine the performance improvement arisen from fusion of texture and geometry, especially on data with spontaneous emotions. The majority of existing approaches are evaluated on databases with posed or induced facial expressions collected in laboratory environments, whereas little attention has been paid on recognizing naturalistic facial expressions on real-world data. This thesis investigates techniques for building robust and high performance FER systems based on a number of established feature sets. It comprises of contributions towards three main objectives: (1) Robustness to face localization errors and facial occlusions. An approach is proposed to handle face localization errors and facial occlusions using Gabor based templates. Template extraction algorithms are designed to collect a pool of local template features and template matching is then performed to covert these templates into distances, which are robust to localization errors and occlusions. (2) Improvement of performance through feature comparison, selection and fusion. A comparative framework is presented to compare the performance between different features and different feature selection algorithms, and examine the performance improvement arising from fusion of texture and geometry. The framework is evaluated for both discrete and dimensional expression recognition on spontaneous data. (3) Evaluation of performance in the context of real-world applications. A system is selected and applied into discriminating posed versus spontaneous expressions and recognizing naturalistic facial expressions. A database is collected from real-world recordings and is used to explore feature differences between standard database images and real-world images, as well as between real-world images and real-world video frames. The performance evaluations are based on the JAFFE, CK, Feedtum, NVIE, Semaine and self-collected QUT databases. The results demonstrate high robustness of the proposed approach to the simulated localization errors and occlusions. Texture and geometry have different contributions to the performance of discrete and dimensional expression recognition, as well as posed versus spontaneous emotion discrimination. These investigations provide useful insights into enhancing robustness and achieving high performance of FER systems, and putting them into real-world applications.

A database for person re-identification in multi-camera surveillance networks

Person re-identification involves recognising individuals in different locations across a network of cameras and is a challenging task due to a large number of varying factors such as pose (both subject and camera) and ambient lighting conditions. Existing databases do not adequately capture these variations, making evaluations of proposed techniques difficult. In this paper, we present a new challenging multi-camera surveillance database designed for the task of person re-identification. This database consists of 150 unscripted sequences of subjects travelling in a building environment though up to eight camera views, appearing from various angles and in varying illumination conditions. A flexible XML-based evaluation protocol is provided to allow a highly configurable evaluation setup, enabling a variety of scenarios relating to pose and lighting conditions to be evaluated. A baseline person re-identification system consisting of colour, height and texture models is demonstrated on this database.

Real-time power line extraction from unmanned aerial system video images

In this paper a real-time vision based power line extraction solution is investigated for active UAV guidance. The line extraction algorithm starts from ridge points detected by steerable filters. A collinear line segments fitting algorithm is followed up by considering global and local information together with multiple collinear measurements. GPU boosted algorithm implementation is also investigated in the experiment. The experimental result shows that the proposed algorithm outperforms two baseline line detection algorithms and is able to fitting long collinear line segments. The low computational cost of the algorithm make suitable for real-time applications.

A portable approach for the surveillance of dengue virus-infected mosquitoes

Dengue virus is the most significant human viral pathogen spread by the bite of an infected mosquito. With no vaccine or antiviral therapy currently available, disease prevention relies largely on surveillance and mosquito control. Preventing the onset of dengue outbreaks and effective vector management would be considerably enhanced through surveillance of dengue virus prevalence in natural mosquito populations. However, current approaches to the identification of virus in field-caught mosquitoes require relatively slow and labor intensive techniques such as virus isolation or RT-PCR involving specialized facilities and personnel. A rapid and portable method for detecting dengue virus-infected mosquitoes is described. Using a hand held battery operated homogenizer and a dengue diagnostic rapid strip the viral protein NS1 was detected as a marker of dengue virus infection. This method could be performed in less than 30 min in the field, requiring no downstream processing, and is able to detect a single infected mosquito in a pool of at least 50 uninfected mosquitoes. The method described in this study allows rapid, real-time monitoring of dengue virus presence in mosquito populations and could be a useful addition to effective monitoring and vector control responses.

Quality based frame selection for video face recognition

Quality based frame selection is a crucial task in video face recognition, to both improve the recognition rate and to reduce the computational cost. In this paper we present a framework that uses a variety of cues (face symmetry, sharpness, contrast, closeness of mouth, brightness and openness of the eye) to select the highest quality facial images available in a video sequence for recognition. Normalized feature scores are fused using a neural network and frames with high quality scores are used in a Local Gabor Binary Pattern Histogram Sequence based face recognition system. Experiments on the Honda/UCSD database shows that the proposed method selects the best quality face images in the video sequence, resulting in improved recognition performance.

Flourishing and video games

Studies dedicated to understanding the relationship between gaming and mental health, have traditionally focused on the effects of depression, anxiety, obsessive usage, aggression, obesity, and faltering ‘real life’ relationships. The complexity of game genre and personality aside, this review aims to define a space for a positive relationship between videogame play and wellbeing by applying current videogame research to the criteria that defines the wellbeing construct ‘flourishing’. Self- determination theory (SDT), and flow provide context, and areas of overlap are explored.

Surveillance of dengue fever virus: a review of epidemiological models and early warning systems

Dengue fever is one of the world’s most important vector-borne diseases. The transmission area of this disease continues to expand due to many factors including urban sprawl, increased travel and global warming. Current preventative techniques are primarily based on controlling mosquito vectors as other prophylactic measures, such as a tetravalent vaccine are unlikely to be available in the foreseeable future. However, the continually increasing dengue incidence suggests that this strategy alone is not sufficient. Epidemiological models attempt to predict future outbreaks using information on the risk factors of the disease. Through a systematic literature review, this paper aims at analyzing the different modeling methods and their outputs in terms of accurately predicting disease outbreaks. We found that many previous studies have not sufficiently accounted for the spatio-temporal features of the disease in the modeling process. Yet with advances in technology, the ability to incorporate such information as well as the socio-environmental aspect allowed for its use as an early warning system, albeit limited geographically to a local scale.

Between formal and informal : the shakeout in China’s online video industry

While a rich body of literature in television and film studies and media policy studies has tended to focus on the media activities in the formal sector, we know much less about informal media activities, its influence on state policies, as well as the dynamics between the formal and the informal sectors. This article examines these issues with reference to a particularly revealing period following a large-scale government crackdown on peer-to-peer video sharing sites in China in 2008. By analyzing the aim and consequences of the state action, I point to the counter-productive effect in terms of cultural loss and the resurgence of offline piracy; and show the positive impact on forcing the informal into the formal sector, and pressuring the formal to innovate. Meanwhile, an increasing rapprochement between professional and user-created content leads to a new relationship between formal and informal sectors. This case demonstrates the importance of considering the dynamics between the two sectors. It also offers compelling evidence of the role of the informal sector in engendering state action, which in turn impacted on the co-evolution of the formal and the informal sectors.

Welfare reform, surveillance and new paternalism

This paper discusses the situation of welfare claimants, often constructed as faulty citizens and flawed welfare subjects. Many are on the receiving end of complex, multi-layered forms of surveillance aimed at securing socially responsible and compliant behaviours. In Australia, as in other Western countries, neoliberal economic regimes with their harsh and often repressive treatment of welfare recipients operate in tandem with a burgeoning and costly arsenal of CCTV and other surveillance and governance assemblages. The Australian Government’s Centrelink BasicsCard is but one example of welfare surveillance, whereby a percentage of a welfare claimant’s allowances must be spent on ‘approved’ items. The BasicsCard which has perhaps slipped under the radar of public discussion and is expanding nationally, raises significant questions about whether it is possible to encourage people to take responsibility for themselves if they no longer have real control over the most important aspects of their lives. Resistance and critical feedback, particularly from Indigenous people, points to a loss of dignity around the imposition of income management, operational complexity and denial of individual agency in using the BasicsCard, alongside the contradiction of apparently becoming ‘self-reliant’ through being income managed by the welfare state. This paper highlights the lack of solid evidence for the implementation/imposition of the BasicsCard and points to the importance of developing critically based research to inform the enactment of evidence based policy, also acting as a touchstone for governmental accountability. In highlighting issues around the BasicsCard this paper makes a contribution to the largely under discussed area of income management and the growth of welfare surveillance in Australia.

Post-discharge surveillance : can patients reliably diagnose surgical wound infections?

Post-discharge surgical wound infection surveillance is an important part of many infection control programs. It is frequently undertaken by patient self-assessment, prompted either by a telephone or postal questionnaire. To assess the reliability of this method, 290 patients were followed for six weeks postoperatively. Their wounds were photographed and also covertly assessed for signs of infection by two experienced infection control nurses (ICNs). Patients also responded to a postal questionnaire seeking evidence of infection at both week four and week six post-surgery. Correlation between the patient's assessment of their wound and the ICNs diagnosis was poor (r=0.37) with a low positive predictive value (28.7%), although negative predictive value was high (98.2%). Assessment of photos for signs of infection by two experienced clinicians also correlated poorly with the ICNs diagnosis of infection (r=0.54). The patient's recall of prescription of an antibiotic by their general practitioner (GP) for wound infection during the postoperative period correlated best with the ICNs diagnosis (r=0.76). This latter measure, particularly when confirmed by the GP in those patients reporting an infection, appears to provide the most valid and resource efficient marker of post-discharge surgical wound infection.