157 research outputs found
Automatic vision based fault detection on electricity transmission components using very highresolution
Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesElectricity is indispensable to modern-day governments and citizenry’s day-to-day operations.
Fault identification is one of the most significant bottlenecks faced by Electricity transmission and
distribution utilities in developing countries to deliver credible services to customers and ensure
proper asset audit and management for network optimization and load forecasting. This is due to
data scarcity, asset inaccessibility and insecurity, ground-surveys complexity, untimeliness, and
general human cost. In this context, we exploit the use of oblique drone imagery with a high spatial
resolution to monitor four major Electric power transmission network (EPTN) components
condition through a fine-tuned deep learning approach, i.e., Convolutional Neural Networks
(CNNs). This study explored the capability of the Single Shot Multibox Detector (SSD), a onestage
object detection model on the electric transmission power line imagery to localize, classify
and inspect faults present. The components fault considered include the broken insulator plate,
missing insulator plate, missing knob, and rusty clamp. The adopted network used a CNN based
on a multiscale layer feature pyramid network (FPN) using aerial image patches and ground truth
to localise and detect faults via a one-phase procedure. The SSD Rest50 architecture variation
performed the best with a mean Average Precision of 89.61%. All the developed SSD based
models achieve a high precision rate and low recall rate in detecting the faulty components, thus
achieving acceptable balance levels F1-score and representation. Finally, comparable to other
works of literature within this same domain, deep-learning will boost timeliness of EPTN inspection
and their component fault mapping in the long - run if these deep learning architectures are widely
understood, adequate training samples exist to represent multiple fault characteristics; and the
effects of augmenting available datasets, balancing intra-class heterogeneity, and small-scale
datasets are clearly understood
Pattern Recognition
A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition
Hidden Citations Obscure True Impact in Science
References, the mechanism scientists rely on to signal previous knowledge,
lately have turned into widely used and misused measures of scientific impact.
Yet, when a discovery becomes common knowledge, citations suffer from
obliteration by incorporation. This leads to the concept of hidden citation,
representing a clear textual credit to a discovery without a reference to the
publication embodying it. Here, we rely on unsupervised interpretable machine
learning applied to the full text of each paper to systematically identify
hidden citations. We find that for influential discoveries hidden citations
outnumber citation counts, emerging regardless of publishing venue and
discipline. We show that the prevalence of hidden citations is not driven by
citation counts, but rather by the degree of the discourse on the topic within
the text of the manuscripts, indicating that the more discussed is a discovery,
the less visible it is to standard bibliometric analysis. Hidden citations
indicate that bibliometric measures offer a limited perspective on quantifying
the true impact of a discovery, raising the need to extract knowledge from the
full text of the scientific corpus
Advances in Object and Activity Detection in Remote Sensing Imagery
The recent revolution in deep learning has enabled considerable development in the fields of object and activity detection. Visual object detection tries to find objects of target classes with precise localisation in an image and assign each object instance a corresponding class label. At the same time, activity recognition aims to determine the actions or activities of an agent or group of agents based on sensor or video observation data. It is a very important and challenging problem to detect, identify, track, and understand the behaviour of objects through images and videos taken by various cameras. Together, objects and their activity recognition in imaging data captured by remote sensing platforms is a highly dynamic and challenging research topic. During the last decade, there has been significant growth in the number of publications in the field of object and activity recognition. In particular, many researchers have proposed application domains to identify objects and their specific behaviours from air and spaceborne imagery. This Special Issue includes papers that explore novel and challenging topics for object and activity detection in remote sensing images and videos acquired by diverse platforms
Autonomous Vehicles
This edited volume, Autonomous Vehicles, is a collection of reviewed and relevant research chapters, offering a comprehensive overview of recent developments in the field of vehicle autonomy. The book comprises nine chapters authored by various researchers and edited by an expert active in the field of study. All chapters are complete in itself but united under a common research study topic. This publication aims to provide a thorough overview of the latest research efforts by international authors, open new possible research paths for further novel developments, and to inspire the younger generations into pursuing relevant academic studies and professional careers within the autonomous vehicle field
Understanding user interactivity for the next-generation immersive communication: design, optimisation, and behavioural analysis
Recent technological advances have opened the gate to a novel way to communicate remotely still feeling connected. In these immersive communications, humans are at the centre of virtual or augmented reality with a full sense of immersion and the possibility to interact with the new environment as well as other humans virtually present. These next-generation communication systems hide a huge potential that can invest in major economic sectors. However, they also posed many new technical challenges, mainly due to the new role of the final user: from merely passive to fully active in requesting and interacting with the content. Thus, we need to go beyond the traditional quality of experience research and develop user-centric solutions, in which the whole multimedia experience is tailored to the final interactive user. With this goal in mind, a better understanding of how people interact with immersive content is needed and it is the focus of this thesis.
In this thesis, we study the behaviour of interactive users in immersive experiences and its impact on the next-generation multimedia systems. The thesis covers a deep literature review on immersive services and user centric solutions, before develop- ing three main research strands. First, we implement novel tools for behavioural analysis of users navigating in a 3-DoF Virtual Reality (VR) system. In detail, we study behavioural similarities among users by proposing a novel clustering algorithm. We also introduce information-theoretic metrics for quantifying similarities for the same viewer across contents. As second direction, we show the impact and advantages of taking into account user behaviour in immersive systems. Specifically, we formulate optimal user centric solutions i) from a server-side perspective and ii) a navigation aware adaptation logic for VR streaming platforms. We conclude by exploiting the aforementioned behavioural studies towards a more in- interactive immersive technology: a 6-DoF VR. Overall in this thesis, experimental results based on real navigation trajectories show key advantages of understanding any hidden patterns of user interactivity to be eventually exploited in engineering user centric solutions for immersive systems
Automated Low-Cost Malaria Detection System in Thin Blood Slide Images Using Mobile Phones
Malaria, a deadly disease which according to the World Health Organisation (WHO) is responsible for the fatal illness in 200 million people around the world in 2010, is diagnosed using peripheral blood examination. The work undertaken in this research programme aims to develop an automated malaria parasite-detection system, using microscopic-image processing, that can be incorporated onto mobile phones. In this research study, the main objective is to achieve the performance equal to or better than the manual microscopy, which is the gold standard in malaria diagnosis, in order to produce a reliable automated diagnostic platform without expert intervention, for the effective treatment and eradication of the deadly disease.
The work contributed to the field of mathematical morphology by proposing a novel method called the Annular Ring Ratio transform for blood component identification. It has also proposed an automated White Blood Cell and Red Blood Cell differentiation algorithm, which when combined with ARR transform method, has wide applications not only for malaria diagnosis but also for many blood related analysis involving microscopic examination.
The research has undertaken investigations on infected cell identification which aids in the calculation of parasitemia, the measure of infection. In addition, an automated diagnostic tool to detect the sexual stage (gametocytes) of the species P.falciparum for post-treatment malaria diagnosis was developed. Furthermore, a parallel investigation was carried out on automated malaria diagnosis on fluorescent thin blood films and a WBC and infected cell differentiation algorithm was proposed.
Finally, a mobile phone application based on the morphological image processing algorithms proposed in this thesis was developed. A complete malaria diagnostic unit using the mobile phones attached to a portable microscope was set up which has enormous potential not only for malaria diagnosis but also for the blood parasitological field where advancement in medical diagnostics using cellular smart phone technology is widely acknowledged
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An Investigation into the Performance of Ethnicity Verification Between Humans and Machine Learning Algorithms
There has been a significant increase in the interest for the task of classifying
demographic profiles i.e. race and ethnicity. Ethnicity is a significant human
characteristic and applying facial image data for the discrimination of ethnicity is
integral to face-related biometric systems. Given the diversity in the application
of ethnicity-specific information such as face recognition and iris recognition, and
the availability of image datasets for more commonly available human
populations, i.e. Caucasian, African-American, Asians, and South-Asian Indians.
A gap has been identified for the development of a system which analyses the
full-face and its individual feature-components (eyes, nose and mouth), for the
Pakistani ethnic group. An efficient system is proposed for the verification of the
Pakistani ethnicity, which incorporates a two-tier (computer vs human) approach.
Firstly, hand-crafted features were used to ascertain the descriptive nature of a
frontal-image and facial profile, for the Pakistani ethnicity. A total of 26 facial
landmarks were selected (16 frontal and 10 for the profile) and by incorporating
2 models for redundant information removal, and a linear classifier for the binary
task. The experimental results concluded that the facial profile image of a
Pakistani face is distinct amongst other ethnicities. However, the methodology
consisted of limitations for example, low performance accuracy, the laborious
nature of manual data i.e. facial landmark, annotation, and the small facial image
dataset. To make the system more accurate and robust, Deep Learning models
are employed for ethnicity classification. Various state-of-the-art Deep models
are trained on a range of facial image conditions, i.e. full face and partial-face
images, plus standalone feature components such as the nose and mouth. Since
ethnicity is pertinent to the research, a novel facial image database entitled
Pakistani Face Database (PFDB), was created using a criterion-specific selection
process, to ensure assurance in each of the assigned class-memberships, i.e.
Pakistani and Non-Pakistani. Comparative analysis between 6 Deep Learning
models was carried out on augmented image datasets, and the analysis
demonstrates that Deep Learning yields better performance accuracy compared
to low-level features. The human phase of the ethnicity classification framework
tested the discrimination ability of novice Pakistani and Non-Pakistani
participants, using a computerised ethnicity task. The results suggest that
humans are better at discriminating between Pakistani and Non-Pakistani full
face images, relative to individual face-feature components (eyes, nose, mouth),
struggling the most with the nose, when making judgements of ethnicity. To
understand the effects of display conditions on ethnicity discrimination accuracy, two conditions were tested; (i) Two-Alternative Forced Choice (2-AFC) and (ii)
Single image procedure. The results concluded that participants perform
significantly better in trials where the target (Pakistani) image is shown alongside
a distractor (Non-Pakistani) image. To conclude the proposed framework,
directions for future study are suggested to advance the current understanding of
image based ethnicity verification.Acumé Forensi
Registration of histology and magnetic resonance imaging of the brain
Combining histology and non-invasive imaging has been attracting the attention of the medical imaging community for a long time, due to its potential to correlate macroscopic information with the underlying microscopic properties of tissues. Histology is an invasive procedure that disrupts the spatial arrangement of the tissue components but enables visualisation and characterisation at a cellular level. In contrast, macroscopic imaging allows non-invasive acquisition of volumetric information but does not provide any microscopic details. Through the establishment of spatial correspondences obtained via image registration, it is possible to compare micro- and macroscopic information and to recover the original histological arrangement in three dimensions. In this thesis, I present: (i) a survey of the literature relative to methods for histology reconstruction with and without the help of 3D medical imaging; (ii) a graph-theoretic method for histology volume reconstruction from sets of 2D sections, without external information; (iii) a method for multimodal 2D linear registration between histology and MRI based on partial matching of shape-informative boundaries
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