Description:
Characterizing of region shapes in digital images is a common requirement in medical image processing. This paper describes an approach based on successive traversals around the region boundary, enabling a sequence of related shape information at different scales to be constructed. The approach is useful in that it allows several different shape characteristics to be determined using the same set of data. The approach and its implementation is described, and an example of its application to a problem in bio-medical cell discrimination is considered and compared with results from more conventional shape characterization techniques.
Description:
This paper proposes a classifier-free method for extraction of power line wires from aerial point cloud data. It combines the advantages of both grid- and point-based processing of the input data. In addition to the non-ground point cloud data, the input to the proposed method includes the pylon locations, which are automatically extracted by a previous method. The proposed method first counts the number of wires in a span between the two successive pylons using two masks: vertical and horizontal. Then, the initial wire segments are obtained and refined iteratively. Finally, the initial segments are extended on both ends and each individual wire points are modelled as a 3D polynomial curve. Experimental results show both the object-based completeness and correctness are 97%, while the point-based completeness and correctness are 99% and 88%, respectively.
Description:
2018 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2018
Description:
Grouping regional towns and cities in Australia based on economic functions could help to better understand the importance of economic factors in determining growth. Different researchers have examined the growth and characteristics of regional cities in Australia. Beer and Maude (1995) examined characteristics and growth rates of Australian regional cities by grouping regional cities with a population greater than 10,000 based on economic functions and using cluster analysis. Cluster analysis is not always an easy task. Beer and Clower (2009) conducted cluster analysis (using Ward‘s hierarchical clustering method) to group regional cities according to industry structure and using the percentage of the workforce employed in each industry sector. Ward‘s clustering method does not inform the researcher which number of clusters provides the most useful grouping for analysis. Researchers therefore need to determine for themselves how many clusters comprise the best or most reasonable solution. As Baum et al. (1999) states ―there is no one best way by which to choose a ‗solution cluster‘
Description:
At Deakin, the Humanities Networked Infrastructure project (HuNI), has paved new ground for facilitating the effective use and re-use of humanities research data. HuNI is one of the first largescale eResearch infrastructure projects for the humanities in Australia and the first national, crossdisciplinary Virtual Laboratory (VL) worldwide. HuNI provides new information infrastructure services for both humanities researchers and members of the public. Its development has been funded by the National eResearch Collaboration Tools and Resources project (NeCTAR) and undertaken by a consortium of thirteen institutions led by Deakin University. A Deakin University Library team with skills in data description, curation, retrieval and preservation is exploring with digital humanities researchers and developers effective means to support and maintain the HuNI project. HuNI ingests and aggregates data from a total of 31 different Australian cultural datasets which cover a wide range of disciplines in the humanities and creative arts. The HuNI VL also provides a number of online research capabilities for humanities researchers to discover and work with the large-scale aggregation of data. The HuNI VL enables researchers to create, save and publish selections of data; to analyse and manipulate the data; share findings and to export the data for reuse in external environments. In a major innovation, HuNI also enables researchers to assert relationships between entities in the form of ‘socially linked’ data. This capability contributes to the building of a ‘vernacular’ network of associations between HuNI records that embody diverse perspectives on knowledge and ramify avenues for research discovery beyond keyword and phrase searches. This paper reports on key milestones in this project, the future role of Libraries as digital humanities research partners and the challenges and sustainability issues that face national digital humanities research projects that are developed in strategic library settings.
Description:
At Deakin, the Humanities Networked Infrastructure project (HuNI), has paved new ground for facilitating the effective use and re-use of humanities research data. HuNI is one of the first largescale eResearch infrastructure projects for the humanities in Australia and the first national, crossdisciplinary Virtual Laboratory (VL) worldwide. HuNI provides new information infrastructure services for both humanities researchers and members of the public. Its development has been funded by the National eResearch Collaboration Tools and Resources project (NeCTAR) and undertaken by a consortium of thirteen institutions led by Deakin University. A Deakin University Library team with skills in data description, curation, retrieval and preservation is exploring with digital humanities researchers and developers effective means to support and maintain the HuNI project. HuNI ingests and aggregates data from a total of 31 different Australian cultural datasets which cover a wide range of disciplines in the humanities and creative arts. The HuNI VL also provides a number of online research capabilities for humanities researchers to discover and work with the large-scale aggregation of data. The HuNI VL enables researchers to create, save and publish selections of data; to analyse and manipulate the data; share findings and to export the data for reuse in external environments. In a major innovation, HuNI also enables researchers to assert relationships between entities in the form of ‘socially linked’ data. This capability contributes to the building of a ‘vernacular’ network of associations between HuNI records that embody diverse perspectives on knowledge and ramify avenues for research discovery beyond keyword and phrase searches. This paper reports on key milestones in this project, the future role of Libraries as digital humanities research partners and the challenges and sustainability issues that face national digital humanities research projects that are developed in strategic library settings.
Description:
Spectral domain optical coherence tomography (SDOCT) is routinely used in the management and diagnosis of a variety of ocular diseases. This imaging modality also finds widespread use in research, where quantitative measurements obtained from the images are used to track disease progression. In recent years, the number of available scanners and imaging protocols grown and there is a distinct absence of a unified tool that is capable of visualizing, segmenting, and analyzing the data. This is especially noteworthy in longitudinal studies, where data from older scanners and/or protocols may need to be analyzed. Here, we present a graphical user interface (GUI) that allows users to visualize and analyze SDOCT images obtained from two commonly used scanners. The retinal surfaces in the scans can be segmented using a previously described method, and the retinal layer thicknesses can be compared to a normative database. If necessary, the segmented surfaces can also be corrected and the changes applied. The interface also allows users to import and export retinal layer thickness data to an SQL database, thereby allowing for the collation of data from a number of collaborating sites.
Description:
This paper presents a comparative study conducted on the performance of the commonly used object tracking and location prediction algorithms for mobile robot navigation in a dynamically cluttered and GPS-denied mining environment. The study was done to test the different algorithms for the same set criteria (such as accuracy and computational time) under the same conditions.The identified commonly used algorithms for object tracking and location prediction of moving objects used in this investigation are Kalman filter (KF), extended Kalman filter (EKF) and particle filter (PF). The study results of those algorithms are analyzed and discussed in this paper. A trade-off was apparent. However, in overall performance KF has shown its competitiveness.The result from the study has found that the KF based algorithm provides better performance in terms of accuracy in tracking dynamic objects under commonly used benchmarks. This finding can be used in development of an efficient robot navigation algorithm.
Description:
Proceedings of the IEEE International Conference on Industrial Technology
Description:
The extraction of Bag of Visual Words (BoVW) features from retinal images for automated classification has been shown to be effective but computationally expensive. Histogram and co-variance matrix features do not generally result in models that have the same predictive accuracy as BoVW and are still computationally expensive. The discovery of features that result in accurate image classification on computationally constrained devices such as smartphones would enable new and promising applications for image classification. For example, smartphone retinal cameras can conceivably make diabetic retinopathy widely available and potentially reduce undiagnosed retinopathy if it could be achieved with computationally simple classification algorithms. A novel image feature extraction technique inspired by N-grams in text mining, called 'Pixel N-grams' is described that can serve this purpose. Results on mammogram and texture classification have shown high accuracy despite the reduced computational complexity. However retinal scan classification results using Pixel N-grams lag behind BoVW approaches. An explanation for the relative poor performance of Pixel N-grams with diabetic retinopathy that draws on concepts associated with the No Free Lunch theorem are presented.
Description:
Texture image classification is very useful in many domains. It has been tried using statistical, spectral and structural approaches. A novel Pixel N-grams technique has emerged for image feature extraction recently. The aim of this paper is to analyse the efficacy of Pixel N-grams technique for texture image classification in comparison with the traditional techniques namely Intensity histogram, Haralick’s features based on co-occurrence matrix and state-of-the-art Bag-of-Visual-Words (BoVW). The experiments were carried out on the benchmark UIUC texture dataset using SVM classifier. The classification performance was compared using Fscore, Recall and Precision. The classification results using Pixel N-gram were significantly better than that using Intensity histogram and Haralick features whereas, they were comparable with the BoVW approach.
Description:
Dielectric insulation of a transformer bushing deteriorates as a function of temperature, oxidation, and moisture. This causes accelerated aging of oil and cellulosic solid insulation, generating fault gases within bushing oil and eventual permanent failure. To prevent such failures, effective analyses and diagnoses need to be investigated. Dissolved Gas Analysis (DGA) can give the indication of internal abnormalities inside the transformer bushing. In addition, Frequency response analysis (FRA) is a widely accepted tool for mechanical deformation diagnosis within power transformers. Although a large number of studies have been conducted on the detection of transformer winding deformation by FRA technique, the impact of bushing faults on the transformer FRA signature has not been sufficiently investigated. It is the goal of this paper to propose precise simulation as well as practical analyses demonstrating the impact of bushing faults on the FRA signature. A real transformer bushing geometry is modelled through 3D finite element analysis (FEM) on which different bushing faults are emulated. To verify the derived simulation results, DGA of transformer oil as well as FRA are performed on a three-phase, 132 kV, 315 MVA power transformer. It can be observed clearly from the results, that bushing faults have an impact on the FRA signature and DGA of the power transformer.
Description:
Sensitivity analysis results are presented to investigate the presence of single–phase rooftop Photovoltaic Cells (PV) in low voltage residential feeders, during short circuits in the overhead lines. The PV rating and location in the feeder and the fault location are considered as the variables of the sensitivity analysis. The single–phase faults are the main focus of this paper and the PV effect on fault current, current in distribution transformer secondary and the voltage at each bus of the feeder are investigated, during fault. Furthermore, to analyze the bus voltages and fault current in the presence of multiple PVs, each with different rating and location, a stochastic analysis is carried out to investigate the expected probability density function of these parameters, considering the uncertainties of PV rating and location as well as fault location.
Description:
Sensitivity analysis results are presented to investigate the presence of single–phase rooftop Photovoltaic Cells (PV) in low voltage residential feeders, during short circuits in the overhead lines. The PV rating and location in the feeder and the fault location are considered as the variables of the sensitivity analysis. The single–phase faults are the main focus of this paper and the PV effect on fault current, current in distribution transformer secondary and the voltage at each bus of the feeder are investigated, during fault. Furthermore, to analyze the bus voltages and fault current in the presence of multiple PVs, each with different rating and location, a stochastic analysis is carried out to investigate the expected probability density function of these parameters, considering the uncertainties of PV rating and location as well as fault location.