The large amounts of Synthetic Aperture Radar (SAR) data now being generated demand automatic tools for image interpretation. Where available, map data provides a valuable aid for visual interpretation and it should aid automatic interpretation. Automatic map based interpretation will be heavily dependent on methods for matching image and map features, both for defining the initial registration and for comparing image and map. This thesis investigates methods for carrying out this matching. Before beginning to develop image map matching methods, a full understanding of the nature of SAR data is first required. The general theory of SAR imaging, the effects of speckle and texture on image statistics, multi-look image statistics, and parameter estimation, are all discussed before addressing the main subject matter. Initially the feasibility of directly matching map features to SAR image features is investigated. Simulations based on a simple image model produce promising results. However, the results of matching features in real images are disappointing. This is due to the limitations of the image model on which matching is based. Possible extensions to include texture and correlation are considered to be computationally too expensive. Rather, it is concluded that pre-processing is needed to structure the image prior to matching. Structuring using edge detection and segmentation are investigated. Among operators for detecting edges in SAR an operator based on intensity ratios is identified as the most suitable. Its performance is fully analysed. Segmentation using an iterative edge detection/segment growing algorithm developed at the Royal Signals and Radar Establishment is investigated and various improvements are suggested. The output of segmentation is structured to a higher level than the output of edge detection. Thus the former is the more suitable candidate for map matching. Approaches to matching segmentations to map data are discussed.

Objective: Although studies have demonstrated that the media-portrayed thin-ideal images and social comparison processes increase body dissatisfaction and negative affect, research has not tested whether women experience pluralistic ignorance by believing that the thin-ideal is an achievable norm. Method: In Study 1, 172 women completed a questionnaire that assessed the extent to which a participant believed that the thin-ideal body image represented the normative body size among women. In Study 2, 356 women participated in a five condition experiment that manipulated the body size of an attractive college student (i.e., thin-ideal or average-sized) and information about the achievability of the woman’s body size (i.e., achievable, not achievable, or no information). Results: Study 1 found no evidence that thin-ideal norm endorsement affected body dissatisfaction or negative affect. Study 2 revealed an increase in body dissatisfaction but not negative affect in the thin-ideal achievable and thin-ideal no information conditions. The results also indicated a marginally significant decrease in negative views of the self in the average-sized achievable and average-sized no information conditions. Furthermore, participants with low self-esteem or poor social support felt better in the average-sized achievable condition when compared to the thin-ideal achievable condition. Also, participants with a higher BMI felt more depressed in the thin-ideal achievable condition when compared to the average-sized achievable condition. Discussion: Results suggest that thin-ideal norm endorsement increased body dissatisfaction by way of social comparative processes and perhaps, pluralistic ignorance. Because participants with low self-esteem or poor social support felt better after seeing an average-sized peer who was said to be the achievable ideal, these results have implications for clinical treatment and prevention interventions. / text

published_or_final_version / Electrical and Electronic Engineering / Doctoral / Doctor of Philosophy

This thesis proposes to learn the absent cognitive element in conventional scene categorization methods: sub-scenes, and use them to better categorize and understand scenes. In scene categorization, it has been observed that the problem of ambiguity occurs when treating the scene as a whole. Scene ambiguity arises from when a similar set of sub-scenes are arranged differently to compose different scenes, or when a scene literally contains several categories. However, these ambiguities can be discerned by the knowledge of sub-scenes. Thus, it is worthy to study sub-scenes and use them to better understand a scene. The proposed research firstly considers an unsupervised method to segment sub-scenes. It emphasizes on generating more integral regions instead of over-segmented regions usually produced by conventional segmentation methods. Several properties of sub-scenes are explored such as proximity grouping, area of influence, similarity and harmony based on psychological principles. These properties are formulated into constraints that are used directly in the proposed framework. A self-determined approach is employed to produce a final segmentation result based on the characteristics of each image in an unsupervised manner. The proposed method performs competitively against other state-of-the-art unsupervised segmentation methods with F-measure of 0.55, Covering of 0.51 and VoI of 1.93 in the Berkeley segmentation dataset. In the Stanford background dataset, it achieves the overlapping score of 0.566 which is higher than the score of 0.499 of the comparison method. To segment and label sub-scenes simultaneously, a supervised approach of semantic segmentation is proposed. It is developed based on a Hierarchical Conditional Random Field classification framework. The proposed method integrates contextual information into the model to improve classification performance. Contextual information including global consistency and spatial context are considered in the proposed method. Global consistency is developed based on generalizing the scene by scene types and spatial context takes the spatial relationship into account. The proposed method improves semantic segmentation by boosting more logical class combinations. It achieves the best score in the MSRC-21 dataset with global accuracy at 87% and the average accuracy at 81%, which out-performs all other state-of-the-art methods by 4% individually. In the Stanford background dataset, it achieves global accuracy at 80.5% and average accuracy at 71.8%, also out-performs other methods by 2%. Finally, the proposed research incorporates sub-scenes into the scene categorization framework to improve categorization performance, especially in ambiguity cases. The proposed method encodes the sub-scene in the way that their spatial information is also considered. Sub-scene descriptor compensates the global descriptor of a scene by evaluating local features with specific geometric attributes. The proposed method obtains an average categorization accuracy of 92.26% in the 8 Scene Category dataset, which outperforms all other published methods by over 2% of improvement. It evaluates ambiguity cases more accurately by discerning which part exemplifies a scene category and how those categories are organized. / published_or_final_version / Electrical and Electronic Engineering / Doctoral / Doctor of Philosophy

The aim of this phenomenological study was to examine the lived experience of choosing professional nursing as a career and to explore the impact that public perception of nursing had on this choice for purposes of informing effective recruitment and retention strategies. Semi-structured interviews of 10 nurses who had been practicing between 11 months and two years were conducted. Five themes emerged from the data: Up Close and Personal/Exposure and Connection, The Image of Nursing, The Conflict Inherent in Nursing, Recruitment and Retention and the Work Environment. From these themes a description of the lived experience of choosing a career in nursing was formed. For these participants, the choice of nursing as a career bespoke a passion that had been affected—but not yet eclipsed—by conflict, compromised fulfillment, and the internalization of nursing and gendered stereotypes directly influenced by the image of nursing. Recommendations involved proposals for the support and preservation of the passion for the profession newer nurses demonstrate as well as health policy initiatives for programs, including a new ad campaign for nursing, that would expose the public to the value of a career in nursing and educate them about the significance and complexities of nursing practice. / text

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University of Macau / Faculty of Science and Technology / Department of Computer and Information Science

University of Macau / Faculty of Science and Technology / Department of Computer and Information Science