1,422 research outputs found
Spectral-spatial classification of n-dimensional images in real-time based on segmentation and mathematical morphology on GPUs
The objective of this thesis is to develop efficient schemes for spectral-spatial n-dimensional image
classification. By efficient schemes, we mean schemes that produce good classification results in
terms of accuracy, as well as schemes that can be executed in real-time on low-cost computing
infrastructures, such as the Graphics Processing Units (GPUs) shipped in personal computers. The
n-dimensional images include images with two and three dimensions, such as images coming from
the medical domain, and also images ranging from ten to hundreds of dimensions, such as the multiand
hyperspectral images acquired in remote sensing.
In image analysis, classification is a regularly used method for information retrieval in areas such as
medical diagnosis, surveillance, manufacturing and remote sensing, among others. In addition, as
the hyperspectral images have been widely available in recent years owing to the reduction in the
size and cost of the sensors, the number of applications at lab scale, such as food quality control, art
forgery detection, disease diagnosis and forensics has also increased. Although there are many
spectral-spatial classification schemes, most are computationally inefficient in terms of execution
time. In addition, the need for efficient computation on low-cost computing infrastructures is
increasing in line with the incorporation of technology into everyday applications.
In this thesis we have proposed two spectral-spatial classification schemes: one based on
segmentation and other based on wavelets and mathematical morphology. These schemes were
designed with the aim of producing good classification results and they perform better than other
schemes found in the literature based on segmentation and mathematical morphology in terms of
accuracy. Additionally, it was necessary to develop techniques and strategies for efficient GPU
computing, for example, a block–asynchronous strategy, resulting in an efficient implementation on
GPU of the aforementioned spectral-spatial classification schemes. The optimal GPU parameters
were analyzed and different data partitioning and thread block arrangements were studied to exploit
the GPU resources. The results show that the GPU is an adequate computing platform for on-board
processing of hyperspectral information
Activity understanding and unusual event detection in surveillance videos
PhDComputer scientists have made ceaseless efforts to replicate cognitive video understanding abilities
of human brains onto autonomous vision systems. As video surveillance cameras become
ubiquitous, there is a surge in studies on automated activity understanding and unusual event detection
in surveillance videos. Nevertheless, video content analysis in public scenes remained a
formidable challenge due to intrinsic difficulties such as severe inter-object occlusion in crowded
scene and poor quality of recorded surveillance footage. Moreover, it is nontrivial to achieve
robust detection of unusual events, which are rare, ambiguous, and easily confused with noise.
This thesis proposes solutions for resolving ambiguous visual observations and overcoming unreliability
of conventional activity analysis methods by exploiting multi-camera visual context
and human feedback.
The thesis first demonstrates the importance of learning visual context for establishing reliable
reasoning on observed activity in a camera network. In the proposed approach, a new Cross
Canonical Correlation Analysis (xCCA) is formulated to discover and quantify time delayed pairwise
correlations of regional activities observed within and across multiple camera views. This
thesis shows that learning time delayed pairwise activity correlations offers valuable contextual
information for (1) spatial and temporal topology inference of a camera network, (2) robust person
re-identification, and (3) accurate activity-based video temporal segmentation. Crucially, in
contrast to conventional methods, the proposed approach does not rely on either intra-camera or
inter-camera object tracking; it can thus be applied to low-quality surveillance videos featuring
severe inter-object occlusions.
Second, to detect global unusual event across multiple disjoint cameras, this thesis extends
visual context learning from pairwise relationship to global time delayed dependency between
regional activities. Specifically, a Time Delayed Probabilistic Graphical Model (TD-PGM) is
proposed to model the multi-camera activities and their dependencies. Subtle global unusual
events are detected and localised using the model as context-incoherent patterns across multiple
camera views. In the model, different nodes represent activities in different decomposed re3
gions from different camera views, and the directed links between nodes encoding time delayed
dependencies between activities observed within and across camera views. In order to learn optimised
time delayed dependencies in a TD-PGM, a novel two-stage structure learning approach
is formulated by combining both constraint-based and scored-searching based structure learning
methods.
Third, to cope with visual context changes over time, this two-stage structure learning approach
is extended to permit tractable incremental update of both TD-PGM parameters and its
structure. As opposed to most existing studies that assume static model once learned, the proposed
incremental learning allows a model to adapt itself to reflect the changes in the current
visual context, such as subtle behaviour drift over time or removal/addition of cameras. Importantly,
the incremental structure learning is achieved without either exhaustive search in a large
graph structure space or storing all past observations in memory, making the proposed solution
memory and time efficient.
Forth, an active learning approach is presented to incorporate human feedback for on-line
unusual event detection. Contrary to most existing unsupervised methods that perform passive
mining for unusual events, the proposed approach automatically requests supervision for critical
points to resolve ambiguities of interest, leading to more robust detection of subtle unusual
events. The active learning strategy is formulated as a stream-based solution, i.e. it makes decision
on-the-fly on whether to request label for each unlabelled sample observed in sequence.
It selects adaptively two active learning criteria, namely likelihood criterion and uncertainty criterion
to achieve (1) discovery of unknown event classes and (2) refinement of classification
boundary.
The effectiveness of the proposed approaches is validated using videos captured from busy
public scenes such as underground stations and traffic intersections
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Automatic detection and classification of leukaemia cells
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Today, there is a substantial number of software and research groups that focus on the development of image processing software to extract useful information from medical images, in order to assist and improve patient diagnosis. The work presented in this thesis is centred on processing of images of blood and bone marrow smears of patients suffering from leukaemia, a common type of cancer. In general, cancer is due to aberrant gene expression, which is caused by either mutations or epigenetic changes in DNA. Poor diet and unhealthy lifestyle may trigger or contribute to these changes, although the underlying mechanism is often unknown. Importantly, many cancer types including leukaemia are curable and patient survival and treatment can be improved, subject to prompt diagnosis. In particular, this study focuses on Acute Myeloid Leukaemia (AML), which can be of eight distinct types (M0 to M7), with the main objective to develop a methodology to automatically detect and classify leukaemia cells into one of the above types. The data was collected from the Department of Haematology, Universiti Sains Malaysia, in Malaysia. Three main methods, namely Cellular Automata, Heuristic Search and classification using Neural Networks are facilitated. In the case of Cellular Automata, an improved method based on the 8-neighbourhood and rules were developed to remove noise from images and estimate the radius of the potential blast cells contained in them. The proposed methodology selects the starting points, corresponding to potential blast cells, for the subsequent seeded heuristic search. The Seeded Heuristic employs a new fitness function for blast cell detection. Furthermore, the WEKA software is utilised for classification of blast cells and hence images, into AML subtypes. As a result accuracy of 97.22% was achieved in the classification of blasts into M3 and other AML subtypes. Finally, these algorithms are integrated into an automated system for image processing. In brief, the research presented in this thesis involves the use of advanced computational techniques for processing and classification of medical images, that is, images of blood samples from patients suffering from leukaemia.The Institute of Higher Education of Malaysia and the Universiti Sains Islam Malaysia (USIM)
Joint segmentation of multivariate time series with hidden process regression for human activity recognition
The problem of human activity recognition is central for understanding and
predicting the human behavior, in particular in a prospective of assistive
services to humans, such as health monitoring, well being, security, etc. There
is therefore a growing need to build accurate models which can take into
account the variability of the human activities over time (dynamic models)
rather than static ones which can have some limitations in such a dynamic
context. In this paper, the problem of activity recognition is analyzed through
the segmentation of the multidimensional time series of the acceleration data
measured in the 3-d space using body-worn accelerometers. The proposed model
for automatic temporal segmentation is a specific statistical latent process
model which assumes that the observed acceleration sequence is governed by
sequence of hidden (unobserved) activities. More specifically, the proposed
approach is based on a specific multiple regression model incorporating a
hidden discrete logistic process which governs the switching from one activity
to another over time. The model is learned in an unsupervised context by
maximizing the observed-data log-likelihood via a dedicated
expectation-maximization (EM) algorithm. We applied it on a real-world
automatic human activity recognition problem and its performance was assessed
by performing comparisons with alternative approaches, including well-known
supervised static classifiers and the standard hidden Markov model (HMM). The
obtained results are very encouraging and show that the proposed approach is
quite competitive even it works in an entirely unsupervised way and does not
requires a feature extraction preprocessing step
Evaluating Scoped Meaning Representations
Semantic parsing offers many opportunities to improve natural language
understanding. We present a semantically annotated parallel corpus for English,
German, Italian, and Dutch where sentences are aligned with scoped meaning
representations in order to capture the semantics of negation, modals,
quantification, and presupposition triggers. The semantic formalism is based on
Discourse Representation Theory, but concepts are represented by WordNet
synsets and thematic roles by VerbNet relations. Translating scoped meaning
representations to sets of clauses enables us to compare them for the purpose
of semantic parser evaluation and checking translations. This is done by
computing precision and recall on matching clauses, in a similar way as is done
for Abstract Meaning Representations. We show that our matching tool for
evaluating scoped meaning representations is both accurate and efficient.
Applying this matching tool to three baseline semantic parsers yields F-scores
between 43% and 54%. A pilot study is performed to automatically find changes
in meaning by comparing meaning representations of translations. This
comparison turns out to be an additional way of (i) finding annotation mistakes
and (ii) finding instances where our semantic analysis needs to be improved.Comment: Camera-ready for LREC 201
A Hybrid Artificial Neural Network Model For Data Visualisation, Classification, And Clustering [QP363.3. T253 2006 f rb].
Tesis ini mempersembahkan penyelidikan tentang satu model hibrid rangkaian neural buatan yang boleh menghasilkan satu peta pengekalan-topologi, serupa dengan penerangan teori bagi peta otak, untuk visualisasi, klasifikasi dan pengklusteran data.
In this thesis, the research of a hybrid Artificial Neural Network (ANN) model that is able to produce a topology-preserving map, which is akin to the theoretical
explanation of the brain map, for data visualisation, classification, and clustering is presented
Smart information retrieval: domain knowledge centric optimization approach
In the age of Internet of Things (IoT), online data has witnessed significant growth in terms of volume and diversity, and research into information retrieval has become one of the important research themes in the Internet oriented data science research. In information retrieval, machine-learning techniques have been widely adopted to automate the challenging process of relation extraction from text data, which is critical to the accuracy and efficiency of information retrieval-based applications including recommender systems and sentiment analysis. In this context, this paper introduces a novel, domain knowledge centric methodology aimed at improving the accuracy of using machine-learning methods for relation classification, and then utilise Genetic Algorithms (GAs) to optimise the feature selection for the learning algorithms. The proposed methodology makes significant contribution to the processes of domain knowledge-based relation extraction including interrogating Linked Open Datasets to generate the relation classification training-data, addressing the imbalanced classification in the training datasets, determining the probability threshold of the best learning algorithm, and establishing the optimum parameters for the genetic algorithm utilised in feature selection. The experimental evaluation of the proposed methodology reveals that the adopted machine-learning algorithms exhibit higher precision and recall in relation extraction in the reduced feature space optimised by the implementation. The considered machine learning includes Support Vector Machine, Perceptron Algorithm Uneven Margin and K-Nearest Neighbours. The outcome is verified by comparing against the Random Mutation Hill-Climbing optimisation algorithm using Wilcoxon signed-rank statistical analysis
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