Improving psychosocial care: a professional development programme

Despite the numerous reports of difficulties experienced by health care providers in providing psychosocial care to terminally ill patients and their families, few studies have yet been undertaken to examine the effectiveness of different educational approaches to addressing these issues. The aim of this paper is to describe a programme of professional development for palliative care nurses, which is currently being offered to 181 registered nurses in Queensland, Australia. The programme is based on an action learning model and is designed to facilitate processes of reflection and peer consultation. In Part One of this paper, a review of this literature is presented to provide the background and rationale for the programme design. Details of the research programme developed to evaluate the programme will be presented in Part Two of this paper, which is to be published in the next issue of this Journal. Surveys of health professionals suggest that the demands of working with terminally ill patients are associated with a great deal of stress (Beaton and Degner 1990, Seale 1992, Vachon 1995), and emotional burden, as they are confronted with their patients' physical and emotional suffering over extended periods of time (Ullrich and Fitzgerald 1990). Key areas of concern (Lyons 1988, Bramwell 1989, Seale 1992, Copp and Dunn 1993, Wilkinson 1995) include: * Handling questions and conversations with dying patients. * Dealing with ethical and moral issues. * Handling emotions. * Giving hope. * Providing spiritual care and bereavement support. * Confronting team communication problems.

Liveness detection based on 3D face shape analysis

In recent years face recognition systems have been applied in various useful applications, such as surveillance, access control, criminal investigations, law enforcement, and others. However face biometric systems can be highly vulnerable to spoofing attacks where an impostor tries to bypass the face recognition system using a photo or video sequence. In this paper a novel liveness detection method, based on the 3D structure of the face, is proposed. Processing the 3D curvature of the acquired data, the proposed approach allows a biometric system to distinguish a real face from a photo, increasing the overall performance of the system and reducing its vulnerability. In order to test the real capability of the methodology a 3D face database has been collected simulating spoofing attacks, therefore using photographs instead of real faces. The experimental results show the effectiveness of the proposed approach.

Shouting from the ivory tower : a marketing approach to improve communication of academic research to entrepreneurs

Evidence-based practice in entrepreneurship requires effective communication of research findings. We focus on how research synopses can “promote” research to entrepreneurs. Drawing on marketing communications literature, we examine how message characteristics of research synopses affect their appeal. We demonstrate the utility of conjoint analysis in this context and find message length, media richness and source credibility to have positive influences. We find mixed support for a hypothesized negative influence of jargon, and for our predictions that participants’ involvement with academic research moderates these effects. Exploratory analyses reveal latent classes of entrepreneurs with differing preferences, particularly for message length and jargon.

Detecting anomalous events at railway level crossings

Collisions between pedestrians and vehicles continue to be a major problem throughout the world. Pedestrians trying to cross roads and railway tracks without any caution are often highly susceptible to collisions with vehicles and trains. Continuous financial, human and other losses have prompted transport related organizations to come up with various solutions addressing this issue. However, the quest for new and significant improvements in this area is still ongoing. This work addresses this issue by building a general framework using computer vision techniques to automatically monitor pedestrian movements in such high-risk areas to enable better analysis of activity, and the creation of future alerting strategies. As a result of rapid development in the electronics and semi-conductor industry there is extensive deployment of CCTV cameras in public places to capture video footage. This footage can then be used to analyse crowd activities in those particular places. This work seeks to identify the abnormal behaviour of individuals in video footage. In this work we propose using a Semi-2D Hidden Markov Model (HMM), Full-2D HMM and Spatial HMM to model the normal activities of people. The outliers of the model (i.e. those observations with insufficient likelihood) are identified as abnormal activities. Location features, flow features and optical flow textures are used as the features for the model. The proposed approaches are evaluated using the publicly available UCSD datasets, and we demonstrate improved performance using a Semi-2D Hidden Markov Model compared to other state of the art methods. Further we illustrate how our proposed methods can be applied to detect anomalous events at rail level crossings.

Histogram of weighted local directions for gait recognition

In this paper, we explore the effectiveness of patch-based gradient feature extraction methods when applied to appearance-based gait recognition. Extending existing popular feature extraction methods such as HOG and LDP, we propose a novel technique which we term the Histogram of Weighted Local Directions (HWLD). These 3 methods are applied to gait recognition using the GEI feature, with classification performed using SRC. Evaluations on the CASIA and OULP datasets show significant improvements using these patch-based methods over existing implementations, with the proposed method achieving the highest recognition rate for the respective datasets. In addition, the HWLD can easily be extended to 3D, which we demonstrate using the GEV feature on the DGD dataset, observing improvements in performance.

Scene invariant multi camera crowd counting

Automated crowd counting has become an active field of computer vision research in recent years. Existing approaches are scene-specific, as they are designed to operate in the single camera viewpoint that was used to train the system. Real world camera networks often span multiple viewpoints within a facility, including many regions of overlap. This paper proposes a novel scene invariant crowd counting algorithm that is designed to operate across multiple cameras. The approach uses camera calibration to normalise features between viewpoints and to compensate for regions of overlap. This compensation is performed by constructing an 'overlap map' which provides a measure of how much an object at one location is visible within other viewpoints. An investigation into the suitability of various feature types and regression models for scene invariant crowd counting is also conducted. The features investigated include object size, shape, edges and keypoints. The regression models evaluated include neural networks, K-nearest neighbours, linear and Gaussian process regresion. Our experiments demonstrate that accurate crowd counting was achieved across seven benchmark datasets, with optimal performance observed when all features were used and when Gaussian process regression was used. The combination of scene invariance and multi camera crowd counting is evaluated by training the system on footage obtained from the QUT camera network and testing it on three cameras from the PETS 2009 database. Highly accurate crowd counting was observed with a mean relative error of less than 10%. Our approach enables a pre-trained system to be deployed on a new environment without any additional training, bringing the field one step closer toward a 'plug and play' system.

Characterisation of microvasulature changes after closed soft tissue trauma by contrast enhanced micro-CT imaging

INTRODUCTION It is known that the vascular morphology and functionality are changed following closed soft tissue trauma (CSTT) [1], and bone fractures [2]. The disruption of blood vessels may lead to hypoxia and necrosis. Currently, most clinical methods for the diagnosis and monitoring of CSTT with or without bone fractures are primarily based on qualitative measures or practical experience, making the diagnosis subjective and inaccurate. There is evidence that CSTT and early vascular changes following the injury delay the soft tissue tissue and bone healing [3]. However, a precise qualitative and quantitative morphological assessment of vasculature changes after trauma is currently missing. In this research, we aim to establish a diagnostic framework to assess the 3D vascular morphological changes after standardized CSTT in a rat model qualitatively and quantitatively using contrast-enhanced micro-CT imaging. METHODS An impact device was used for the application of a controlled reproducible CSTT to the left thigh (Biceps Femoris) of anaesthetized male Wistar rats. After euthanizing the animals at 6 hours, 24 hours, 3 days, 7 days, or 14 days after trauma, CSTT was qualitatively evaluated by macroscopic visual observation of the skin and muscles. For visualization of the vasculature, the blood vessels of sacrificed rats were flushed with heparinised saline and then perfused with a radio-opaque contrast agent (Microfil, MV 122, Flowtech, USA) using an infusion pump. After allowing the contrast agent to polymerize overnight, both hind-limbs were dissected, and then the whole injured and contra-lateral control limbs were imaged using a micro-CT scanner (µCT 40, Scanco Medical, Switzerland) to evaluate the vascular morphological changes. Correlated biopsy samples were also taken from the CSTT region of both injured and control legs. The morphological parameters such as the vessel volume ratio (VV/TV), vessel diameter (V.D), spacing (V.Sp), number (V.N), connectivity (V.Conn) and the degree of anisotropy (DA) were then quantified by evaluating the scans of biopsy samples using the micro-CT imaging system. RESULTS AND DISCUSSION A qualitative evaluation of the CSTT has shown that the developed impact protocols were capable of producing a defined and reproducible injury within the region of interest (ROI), resulting in a large hematoma and moderate swelling in both lateral and medial sides of the injured legs. Also, the visualization of the vascular network using 3D images confirmed the ability to perfuse the large vessels and a majority of the microvasculature consistently (Figure 1). Quantification of the vascular morphology obtained from correlated biopsy samples has demonstrated that V.D and V.N and V.Sp were significantly higher in the injured legs 24 hours after impact in comparison with the control legs (p<0.05). The evaluation of the other time points is currently progressing. CONCLUSIONS The findings of this research will contribute to a better understanding of the changes to the vascular network architecture following traumatic injuries and during healing process. When interpreted in context of functional changes, such as tissue oxygenation, this will allow for objective diagnosis and monitoring of CSTT and serve as validation for future non-invasive clinical assessment modalities.

The effect of consumer education on unit price usage : an early exploratory study

Consumer awareness and usage of Unit Price (UP) information continues to hold academic interest. Originally designed as a device to enable shoppers to make comparisons between grocery products, it is argued consumers still lack a sufficient understanding of the device. Previous research has tended to focus on product choice, effect of time, and structural changes to price presentation. No studies have tested the effect of UP consumer education on grocery shopping expenditure. Supported by distributed learning theories, this is the first study to condition participants over a twenty week period, to comprehend and employ UP information while shopping. A 3x5 mixed factorial design was employed to collect data from 357 shoppers. A 3 (Control, Massed, Spaced) x 5 (Time Point: Week 0, 5, 10, 15 and 20) mixed factorial analysis of variance (ANOVA) was performed to analyse the data. Preliminary results revealed that the three groups differed in their average expenditure over the twenty weeks. The Control group remained stable across the five time points. Results indicated that both intensive (Massed) and less intensive (Spaced) exposure to UP information achieved similar results, with both group reducing average expenditure similarly by Week 5. These patterns held for twenty weeks, with conditioned groups reducing their grocery expenditure by over 10%. This research has academic value as a test of applied learning theories. We argue, retailers can attain considerable market advantages as efforts to enhance customers’ knowledge, through consumer education campaigns, can have a positive and strong impact on customer trust and goodwill toward the organisation. Hence, major practical implications for both regulators and retailers exist.

Real-time video event detection in crowded scenes using MPEG derived features : a multiple instance learning approach

This paper presents an investigation into event detection in crowded scenes, where the event of interest co-occurs with other activities and only binary labels at the clip level are available. The proposed approach incorporates a fast feature descriptor from the MPEG domain, and a novel multiple instance learning (MIL) algorithm using sparse approximation and random sensing. MPEG motion vectors are used to build particle trajectories that represent the motion of objects in uniform video clips, and the MPEG DCT coefficients are used to compute a foreground map to remove background particles. Trajectories are transformed into the Fourier domain, and the Fourier representations are quantized into visual words using the K-Means algorithm. The proposed MIL algorithm models the scene as a linear combination of independent events, where each event is a distribution of visual words. Experimental results show that the proposed approaches achieve promising results for event detection compared to the state-of-the-art.

An evaluation of different features and learning models for anomalous event detection

The huge amount of CCTV footage available makes it very burdensome to process these videos manually through human operators. This has made automated processing of video footage through computer vision technologies necessary. During the past several years, there has been a large effort to detect abnormal activities through computer vision techniques. Typically, the problem is formulated as a novelty detection task where the system is trained on normal data and is required to detect events which do not fit the learned ‘normal’ model. There is no precise and exact definition for an abnormal activity; it is dependent on the context of the scene. Hence there is a requirement for different feature sets to detect different kinds of abnormal activities. In this work we evaluate the performance of different state of the art features to detect the presence of the abnormal objects in the scene. These include optical flow vectors to detect motion related anomalies, textures of optical flow and image textures to detect the presence of abnormal objects. These extracted features in different combinations are modeled using different state of the art models such as Gaussian mixture model(GMM) and Semi- 2D Hidden Markov model(HMM) to analyse the performances. Further we apply perspective normalization to the extracted features to compensate for perspective distortion due to the distance between the camera and objects of consideration. The proposed approach is evaluated using the publicly available UCSD datasets and we demonstrate improved performance compared to other state of the art methods.

Quality based frame selection for face clustering in news video

Clustering identities in a broadcast video is a useful task to aid in video annotation and retrieval. Quality based frame selection is a crucial task in video face clustering, to both improve the clustering performance and reduce the computational cost. We present a frame work that selects the highest quality frames available in a video to cluster the face. This frame selection technique is based on low level and high level features (face symmetry, sharpness, contrast and brightness) to select the highest quality facial images available in a face sequence for clustering. We also consider the temporal distribution of the faces to ensure that selected faces are taken at times distributed throughout the sequence. Normalized feature scores are fused and frames with high quality scores are used in a Local Gabor Binary Pattern Histogram Sequence based face clustering system. We present a news video database to evaluate the clustering system performance. Experiments on the newly created news database show that the proposed method selects the best quality face images in the video sequence, resulting in improved clustering performance.

Optimal camera planning under versatile user constraints in multi-camera image processing systems

The selection of optimal camera configurations (camera locations, orientations, etc.) for multi-camera networks remains an unsolved problem. Previous approaches largely focus on proposing various objective functions to achieve different tasks. Most of them, however, do not generalize well to large scale networks. To tackle this, we propose a statistical framework of the problem as well as propose a trans-dimensional simulated annealing algorithm to effectively deal with it. We compare our approach with a state-of-the-art method based on binary integer programming (BIP) and show that our approach offers similar performance on small scale problems. However, we also demonstrate the capability of our approach in dealing with large scale problems and show that our approach produces better results than two alternative heuristics designed to deal with the scalability issue of BIP. Last, we show the versatility of our approach using a number of specific scenarios.

Semi-binary based video features for activity representation

Efficient and effective feature detection and representation is an important consideration when processing videos, and a large number of applications such as motion analysis, 3D scene understanding, tracking etc. depend on this. Amongst several feature description methods, local features are becoming increasingly popular for representing videos because of their simplicity and efficiency. While they achieve state-of-the-art performance with low computational complexity, their performance is still too limited for real world applications. Furthermore, rapid increases in the uptake of mobile devices has increased the demand for algorithms that can run with reduced memory and computational requirements. In this paper we propose a semi binary based feature detectordescriptor based on the BRISK detector, which can detect and represent videos with significantly reduced computational requirements, while achieving comparable performance to the state of the art spatio-temporal feature descriptors. First, the BRISK feature detector is applied on a frame by frame basis to detect interest points, then the detected key points are compared against consecutive frames for significant motion. Key points with significant motion are encoded with the BRISK descriptor in the spatial domain and Motion Boundary Histogram in the temporal domain. This descriptor is not only lightweight but also has lower memory requirements because of the binary nature of the BRISK descriptor, allowing the possibility of applications using hand held devices.We evaluate the combination of detectordescriptor performance in the context of action classification with a standard, popular bag-of-features with SVM framework. Experiments are carried out on two popular datasets with varying complexity and we demonstrate comparable performance with other descriptors with reduced computational complexity.

Evaluation of two-view geometry methods with automatic ground-truth generation

A large number of methods have been published that aim to evaluate various components of multi-view geometry systems. Most of these have focused on the feature extraction, description and matching stages (the visual front end), since geometry computation can be evaluated through simulation. Many data sets are constrained to small scale scenes or planar scenes that are not challenging to new algorithms, or require special equipment. This paper presents a method for automatically generating geometry ground truth and challenging test cases from high spatio-temporal resolution video. The objective of the system is to enable data collection at any physical scale, in any location and in various parts of the electromagnetic spectrum. The data generation process consists of collecting high resolution video, computing accurate sparse 3D reconstruction, video frame culling and down sampling, and test case selection. The evaluation process consists of applying a test 2-view geometry method to every test case and comparing the results to the ground truth. This system facilitates the evaluation of the whole geometry computation process or any part thereof against data compatible with a realistic application. A collection of example data sets and evaluations is included to demonstrate the range of applications of the proposed system.