Towards automatic power line detection for a UAV surveillance system using pulse coupled neural filter and an improved Hough transform

Spatial information captured from optical remote sensors on board unmanned aerial vehicles (UAVs) has great potential in automatic surveillance of electrical infrastructure. For an automatic vision-based power line inspection system, detecting power lines from a cluttered background is one of the most important and challenging tasks. In this paper, a novel method is proposed, specifically for power line detection from aerial images. A pulse coupled neural filter is developed to remove background noise and generate an edge map prior to the Hough transform being employed to detect straight lines. An improved Hough transform is used by performing knowledge-based line clustering in Hough space to refine the detection results. The experiment on real image data captured from a UAV platform demonstrates that the proposed approach is effective for automatic power line detection.

Estimation and compensation of clipping noise in OFDMA systems

We propose an efficient and low-complexity scheme for estimating and compensating clipping noise in OFDMA systems. Conventional clipping noise estimation schemes, which need all demodulated data symbols, may become infeasible in OFDMA systems where a specific user may only know his own modulation scheme. The proposed scheme first uses equalized output to identify a limited number of candidate clips, and then exploits the information on known subcarriers to reconstruct clipped signal. Simulation results show that the proposed scheme can significantly improve the system performance.

Improved detection and tracking of objects in surveillance video

Surveillance networks are typically monitored by a few people, viewing several monitors displaying the camera feeds. It is then very difficult for a human operator to effectively detect events as they happen. Recently, computer vision research has begun to address ways to automatically process some of this data, to assist human operators. Object tracking, event recognition, crowd analysis and human identification at a distance are being pursued as a means to aid human operators and improve the security of areas such as transport hubs. The task of object tracking is key to the effective use of more advanced technologies. To recognize an event people and objects must be tracked. Tracking also enhances the performance of tasks such as crowd analysis or human identification. Before an object can be tracked, it must be detected. Motion segmentation techniques, widely employed in tracking systems, produce a binary image in which objects can be located. However, these techniques are prone to errors caused by shadows and lighting changes. Detection routines often fail, either due to erroneous motion caused by noise and lighting effects, or due to the detection routines being unable to split occluded regions into their component objects. Particle filters can be used as a self contained tracking system, and make it unnecessary for the task of detection to be carried out separately except for an initial (often manual) detection to initialise the filter. Particle filters use one or more extracted features to evaluate the likelihood of an object existing at a given point each frame. Such systems however do not easily allow for multiple objects to be tracked robustly, and do not explicitly maintain the identity of tracked objects. This dissertation investigates improvements to the performance of object tracking algorithms through improved motion segmentation and the use of a particle filter. A novel hybrid motion segmentation / optical flow algorithm, capable of simultaneously extracting multiple layers of foreground and optical flow in surveillance video frames is proposed. The algorithm is shown to perform well in the presence of adverse lighting conditions, and the optical flow is capable of extracting a moving object. The proposed algorithm is integrated within a tracking system and evaluated using the ETISEO (Evaluation du Traitement et de lInterpretation de Sequences vidEO - Evaluation for video understanding) database, and significant improvement in detection and tracking performance is demonstrated when compared to a baseline system. A Scalable Condensation Filter (SCF), a particle filter designed to work within an existing tracking system, is also developed. The creation and deletion of modes and maintenance of identity is handled by the underlying tracking system; and the tracking system is able to benefit from the improved performance in uncertain conditions arising from occlusion and noise provided by a particle filter. The system is evaluated using the ETISEO database. The dissertation then investigates fusion schemes for multi-spectral tracking systems. Four fusion schemes for combining a thermal and visual colour modality are evaluated using the OTCBVS (Object Tracking and Classification in and Beyond the Visible Spectrum) database. It is shown that a middle fusion scheme yields the best results and demonstrates a significant improvement in performance when compared to a system using either mode individually. Findings from the thesis contribute to improve the performance of semi-automated video processing and therefore improve security in areas under surveillance.

Continued use of process modeling grammars : the impact of individual difference factors

Process modeling grammars are used by analysts to describe information systems domains in terms of the business operations an organization is conducting. While prior research has examined the factors that lead to continued usage behavior, little knowledge has been established as to what extent characteristics of the users of process modeling grammars inform usage behavior. In this study, a theoretical model is advanced that incorporates determinants of continued usage behavior as well as key antecedent individual difference factors of the grammar users, such as modeling experience, modeling background and perceived grammar familiarity. Findings from a global survey of 529 grammar users support the hypothesized relationships of the model. The study offers three central contributions. First, it provides a validated theoretical model of post-adoptive modeling grammar usage intentions. Second, it discusses the effects of individual difference factors of grammar users in the context of modeling grammar usage. Third, it provides implications for research and practice.

Modeling and Verification of Privacy Enhancing Protocols

Privacy enhancing protocols (PEPs) are a family of protocols that allow secure exchange and management of sensitive user information. They are important in preserving users’ privacy in today’s open environment. Proof of the correctness of PEPs is necessary before they can be deployed. However, the traditional provable security approach, though well established for verifying cryptographic primitives, is not applicable to PEPs. We apply the formal method of Coloured Petri Nets (CPNs) to construct an executable specification of a representative PEP, namely the Private Information Escrow Bound to Multiple Conditions Protocol (PIEMCP). Formal semantics of the CPN specification allow us to reason about various security properties of PIEMCP using state space analysis techniques. This investigation provides us with preliminary insights for modeling and verification of PEPs in general, demonstrating the benefit of applying the CPN-based formal approach to proving the correctness of PEPs.

Bayesian latent trait modeling of migraine symptom data

Definition of disease phenotype is a necessary preliminary to research into genetic causes of a complex disease. Clinical diagnosis of migraine is currently based on diagnostic criteria developed by the International Headache Society. Previously, we examined the natural clustering of these diagnostic symptoms using latent class analysis (LCA) and found that a four-class model was preferred. However, the classes can be ordered such that all symptoms progressively intensify, suggesting that a single continuous variable representing disease severity may provide a better model. Here, we compare two models: item response theory and LCA, each constructed within a Bayesian context. A deviance information criterion is used to assess model fit. We phenotyped our population sample using these models, estimated heritability and conducted genome-wide linkage analysis using Merlin-qtl. LCA with four classes was again preferred. After transformation, phenotypic trait values derived from both models are highly correlated (correlation = 0.99) and consequently results from subsequent genetic analyses were similar. Heritability was estimated at 0.37, while multipoint linkage analysis produced genome-wide significant linkage to chromosome 7q31-q33 and suggestive linkage to chromosomes 1 and 2. We argue that such continuous measures are a powerful tool for identifying genes contributing to migraine susceptibility.

Enhancing digital road map with lane details extracted from large-scale stereo aerial imagery using object-oriented image analysis

Precise, up-to-date and increasingly detailed road maps are crucial for various advanced road applications, such as lane-level vehicle navigation, and advanced driver assistant systems. With the very high resolution (VHR) imagery from digital airborne sources, it will greatly facilitate the data acquisition, data collection and updates if the road details can be automatically extracted from the aerial images. In this paper, we proposed an effective approach to detect road lane information from aerial images with employment of the object-oriented image analysis method. Our proposed algorithm starts with constructing the DSM and true orthophotos from the stereo images. The road lane details are detected using an object-oriented rule based image classification approach. Due to the affection of other objects with similar spectral and geometrical attributes, the extracted road lanes are filtered with the road surface obtained by a progressive two-class decision classifier. The generated road network is evaluated using the datasets provided by Queensland department of Main Roads. The evaluation shows completeness values that range between 76% and 98% and correctness values that range between 82% and 97%.

Modeling the impact of potential vaccines on epidemics of sexually transmitted chlamydia trachomatis infection

Background. We investigated the likely impact of vaccines on the prevalence of and morbidity due to Chlamydia trachomatis (chlamydia) infections in heterosexual populations. Methods.An individual‐based mathematical model of chlamydia transmission was developed and linked to the infection course in chlamydia‐infected individuals. The model describes the impact of a vaccine through its effect on the chlamydial load required to infect susceptible individuals (the “critical load”), the load in infected individuals, and their subsequent infectiousness. The model was calibrated using behavioral, biological, and clinical data. Results.A fully protective chlamydia vaccine administered before sexual debut can theoretically eliminate chlamydia epidemics within 20 years. Partially effective vaccines can still greatly reduce the incidence of chlamydia infection. Vaccines should aim primarily to increase the critical load in susceptible individuals and secondarily to decrease the peak load and/or the duration of infection in vaccinated individuals who become infected. Vaccinating both sexes has a beneficial impact on chlamydia‐related morbidity, but targeting women is more effective than targeting men. Conclusions.Our findings can be used in laboratory settings to evaluate vaccine candidates in animal models, by regulatory bodies in the promotion of candidates for clinical trials, and by public health authorities in deciding on optimal intervention strategies.

Assessment of tear film surface quality using dynamic-area high-speed videokeratoscopy

A new method for noninvasive assessment of tear film surface quality (TFSQ) is proposed. The method is based on high-speed videokeratoscopy in which the corneal area for the analysis is dynamically estimated in a manner that removes videokeratoscopy interference from the shadows of eyelashes but not that related to the poor quality of the precorneal tear film that is of interest. The separation between the two types of seemingly similar videokeratoscopy interference is achieved by region-based classification in which the overall noise is first separated from the useful signal (unaltered videokeratoscopy pattern), followed by a dedicated interference classification algorithm that distinguishes between the two considered interferences. The proposed technique provides a much wider corneal area for the analysis of TFSQ than the previously reported techniques. A preliminary study with the proposed technique, carried out for a range of anterior eye conditions, showed an effective behavior in terms of noise to signal separation, interference classification, as well as consistent TFSQ results. Subsequently, the method proved to be able to not only discriminate between the bare eye and the lens on eye conditions but also to have the potential to discriminate between the two types of contact lenses.

Modeling corneal surfaces with rational functions for high-speed videokeratoscopy data compression

High-speed videokeratoscopy is an emerging technique that enables study of the corneal surface and tear-film dynamics. Unlike its static predecessor, this new technique results in a very large amount of digital data for which storage needs become significant. We aimed to design a compression technique that would use mathematical functions to parsimoniously fit corneal surface data with a minimum number of coefficients. Since the Zernike polynomial functions that have been traditionally used for modeling corneal surfaces may not necessarily correctly represent given corneal surface data in terms of its optical performance, we introduced the concept of Zernike polynomial-based rational functions. Modeling optimality criteria were employed in terms of both the rms surface error as well as the point spread function cross-correlation. The parameters of approximations were estimated using a nonlinear least-squares procedure based on the Levenberg-Marquardt algorithm. A large number of retrospective videokeratoscopic measurements were used to evaluate the performance of the proposed rational-function-based modeling approach. The results indicate that the rational functions almost always outperform the traditional Zernike polynomial approximations with the same number of coefficients.

Depression, anxiety and body image after treatment for invasive stage one epithelial ovarian cancer

Background: Diagnosis of epithelial ovarian cancer (EOC) in young women has major implications including those to their reproductive potential. We evaluated depression, anxiety and body image in patients with stage I EOC treated with fertility sparing surgery (FSS) or radical surgery (RS). We also investigated fertility outcomes after FSS.----- Methods: A retrospective study was undertaken in which 62 patients completed questionnaires related to anxiety, depression, body image and fertility outcomes. Additional information on adjuvant therapy after FSS and RS and demographic details were abstracted from medical records. Both bi and multivariate regression models were used to assess the relationship between demographic, clinical and pathological results and scores for anxiety, depression and body image.----- Results: Thirty-nine patients underwent RS and the rest, FSS. The percentage of patients reporting elevated anxiety and depression (subscores ≥ 11) were 27 % and 5% respectively. The median (inter quartile range) score for body image scale (BIS) was 6 (3-15). None of the demographic or clinical factors examined showed significant association with anxiety and BIS with the exception of ‘time since diagnosis’. For depression, post-menopausal status was the only independent predictor. Among those 23 patients treated by FSS, 14 patients tried to conceive (7 successful), resulting in 7 live births, one termination of pregnancy and one miscarriage.----- Conclusion: This study shows that psychological issues are common in women treated for stage I EOC. Reproduction after FSS is feasible and lead to the birth of healthy babies in about half of patients who wished to have another child. Further prospective studies with standardised instruments are required.

Design and evaluation of an image analysis platform for low-power, low-bandwidth camera networks

We describe the design and evaluation of a platform for networks of cameras in low-bandwidth, low-power sensor networks. In our work to date we have investigated two different DSP hardware/software platforms for undertaking the tasks of compression and object detection and tracking. We compare the relative merits of each of the hardware and software platforms in terms of both performance and energy consumption. Finally we discuss what we believe are the ongoing research questions for image processing in WSNs.