Description:
Research of clinical patients with a pre-existing psychological disorder involved in a disaster is limited. This study investigated relationships between pre-and post-earthquake psychopathology (i.e., anxiety, depression, and posttraumatic stress), peritraumatic distress, work and social impairment, perceived support post-earthquake, and personality dimensions in an outpatient, anxiety disorder population. Thirty-seven patients with pre-existing anxiety disorders completed standard clinical assessments pre-earthquake. They then completed a second set of questionnaires 3 months after exposure to the 2010 Christchurch, Aotearoa New Zealand, earthquake. Bivariate correlations on the variables determined what relationships were present, and paired samples t-tests assessed differences in pre- and post-earthquake anxiety, depression, and alcohol and drug consumption as well as relationships with peritraumatic distress, posttraumatic stress, and personality variables. Significant relationships were found between pre-earthquake psychopathology, peritraumatic distress, post-earthquake psychopathology, and impairment. Paired samples t-tests demonstrated anxiety and depression scores were significantly lower post-earthquake. However, prior anxiety and depression, as well as peritraumatic distress, were significantly associated with post-earthquake psychopathology, including posttraumatic stress and impaired work and social functioning. There were no differences between pre- and post-event alcohol and drug consumption. The personality dimensions of harm avoidance, self-directedness, and persistence significantly associated with post-disaster anxiety and depression. Promisingly, post-earthquake perceived support was significantly negatively correlated with depression and posttraumatic stress. Keywords: Anxiety, depression, posttraumatic stress, peritraumatic distress, personality temperament, earthquake
Description:
Colour and texture are the most common features used in image processing and computer vision applications. Unlike colour, a local texture descriptor needs to express the unique variation pattern in the intensity differences of pixels in the neighbourhood of the pixel-of-interest (POI) so that it can sufficiently discriminate different textures. Since the descriptor needs spatial manipulation of all pixels in the neighbourhood of the POI, approximation of texture impacts not only the computational cost but also the performance of the applications. In this thesis, we aim to develop novel texture descriptors, especially for hierarchical image segmentation techniques that have recently gained popularity for their wide range of applications in medical imaging, video surveillance, autonomous navigation, and computer vision in general. To pursue the aim, we focus in reducing the length of the texture feature and directly modelling the distribution of intensity-variation in the parametric space of a probability density function (pdf). In the first contributory chapter, we enhance the state-of-the-art Weber local descriptor (WLD) by considering the mean value of neighbouring pixel intensities along radial directions instead of sampling pixels at three scales. Consequently, the proposed descriptor, named Radial Mean WLD (RM-WLD), is three-fold shorter than WLD and it performs slightly better than WLD in hierarchical image segmentation. The statistical distributions of pixel intensities in different image regions are diverse by nature. In the second contributory chapter, we propose a novel texture feature, called ‘joint scale,’ by directly modelling the probability distribution of intensity differences. The Weibull distribution, one of the extreme value distributions, is selected for this purpose as it can represent a wide range of probability distributions with a couple of parameters. In addition, gradient orientation feature is calculated from all pixels in the neighbourhood with an extended Sobel operator, instead of using only the vertical and horizontal neighbours as considered in WLD. The length of the texture descriptor combining joint scale and gradiet orientation features remains the same as RM-WLD, but it exhibits significantly improved discrimination capability for better image segmentation. Initial regions in hierarchical segmentation play an important role in approximating texture features. Traditional arbitrary-shaped initial regions maintain the uniform colour property and thus may not retain the texture pattern of the segment they belong to. In the final contributory chapter, we introduce regular-shaped initial regions by enhancing the cuboidal partitioning technique, which has recently gained popularity in image/video coding research. Since the regions (cuboids) of cuboidal partitioning are of rectangular shape, they do not follow the colour-based boundary adherence of traditional initial regions. Consequently, the cuboids retain sufficient texture pattern cues to provide better texture approximation and discriminating capability. We have used benchmark segmentation datasets and metrics to evaluate the proposed texture descriptors. Experimental results on benchmark metrics and computational time are promising when the proposed texture features are used in the state-of-the-art iterative contraction and merging (ICM) image segmentation technique.