334 research outputs found
WEATHER LORE VALIDATION TOOL USING FUZZY COGNITIVE MAPS BASED ON COMPUTER VISION
Published ThesisThe creation of scientific weather forecasts is troubled by many technological challenges (Stern
& Easterling, 1999) while their utilization is generally dismal. Consequently, the majority of
small-scale farmers in Africa continue to consult some forms of weather lore to reach various
cropping decisions (Baliscan, 2001). Weather lore is a body of informal folklore (Enock, 2013),
associated with the prediction of the weather, and based on indigenous knowledge and human
observation of the environment. As such, it tends to be more holistic, and more localized to the
farmers’ context. However, weather lore has limitations; for instance, it has an inability to offer
forecasts beyond a season. Different types of weather lore exist, utilizing almost all available
human senses (feel, smell, sight and hearing). Out of all the types of weather lore in existence, it
is the visual or observed weather lore that is mostly used by indigenous societies, to come up
with weather predictions.
On the other hand, meteorologists continue to treat this knowledge as superstition, partly because
there is no means to scientifically evaluate and validate it. The visualization and characterization
of visual sky objects (such as moon, clouds, stars, and rainbows) in forecasting weather are
significant subjects of research. To realize the integration of visual weather lore in modern
weather forecasting systems, there is a need to represent and scientifically substantiate this form
of knowledge. This research was aimed at developing a method for verifying visual weather lore that is used by
traditional communities to predict weather conditions. To realize this verification, fuzzy
cognitive mapping was used to model and represent causal relationships between selected visual
weather lore concepts and weather conditions. The traditional knowledge used to produce these
maps was attained through case studies of two communities (in Kenya and South Africa).These
case studies were aimed at understanding the weather lore domain as well as the causal effects
between metrological and visual weather lore. In this study, common astronomical weather lore
factors related to cloud physics were identified as: bright stars, dispersed clouds, dry weather,
dull stars, feathery clouds, gathering clouds, grey clouds, high clouds, layered clouds, low
clouds, stars, medium clouds, and rounded clouds. Relationships between the concepts were also
identified and formally represented using fuzzy cognitive maps. On implementing the verification tool, machine vision was used to recognize sky objects
captured using a sky camera, while pattern recognition was employed in benchmarking and
scoring the objects. A wireless weather station was used to capture real-time weather parameters.
The visualization tool was then designed and realized in a form of software artefact, which
integrated both computer vision and fuzzy cognitive mapping for experimenting visual weather
lore, and verification using various statistical forecast skills and metrics. The tool consists of four
main sub-components: (1) Machine vision that recognizes sky objects using support vector
machine classifiers using shape-based feature descriptors; (2) Pattern recognition–to benchmark
and score objects using pixel orientations, Euclidean distance, canny and grey-level concurrence
matrix; (3) Fuzzy cognitive mapping that was used to represent knowledge (i.e. active hebbian
learning algorithm was used to learn until convergence); and (4) A statistical computing
component was used for verifications and forecast skills including brier score and contingency
tables for deterministic forecasts.
Rigorous evaluation of the verification tool was carried out using independent (not used in the
training and testing phases) real-time images from Bloemfontein, South Africa, and Voi-Kenya.
The real-time images were captured using a sky camera with GPS location services. The results
of the implementation were tested for the selected weather conditions (for example, rain, heat, cold, and dry conditions), and found to be acceptable (the verified prediction accuracies were
over 80%). The recommendation in this study is to apply the implemented method for processing
tasks, towards verifying all other types of visual weather lore. In addition, the use of the method
developed also requires the implementation of modules for processing and verifying other types
of weather lore, such as sounds, and symbols of nature. Since time immemorial, from Australia to Asia, Africa to Latin America, local communities have
continued to rely on weather lore observations to predict seasonal weather as well as its effects
on their livelihoods (Alcock, 2014). This is mainly based on many years of personal experiences
in observing weather conditions. However, when it comes to predictions for longer lead-times
(i.e. over a season), weather lore is uncertain (Hornidge & Antweiler, 2012). This uncertainty has
partly contributed to the current status where meteorologists and other scientists continue to treat
weather lore as superstition (United-Nations, 2004), and not capable of predicting weather.
One of the problems in testing the confidence in weather lore in predicting weather is due to
wide varieties of weather lore that are found in the details of indigenous sayings, which are
tightly coupled to locality and pattern variations(Oviedo et al., 2008). This traditional knowledge
is entrenched within the day-to-day socio-economic activities of the communities using it and is
not globally available for comparison and validation (Huntington, Callaghan, Fox, & Krupnik,
2004). Further, this knowledge is based on local experience that lacks benchmarking techniques;
so that harmonizing and integrating it within the science-based weather forecasting systems is a
daunting task (Hornidge & Antweiler, 2012). It is partly for this reason that the question of
validation of weather lore has not yet been substantially investigated. Sufficient expanded
processes of gathering weather observations, combined with comparison and validation, can produce some useful information. Since forecasting weather accurately is a challenge even with
the latest supercomputers (BBC News Magazine, 2013), validated weather lore can be useful if it
is incorporated into modern weather prediction systems.
Validation of traditional knowledge is a necessary step in the management of building integrated
knowledge-based systems. Traditional knowledge incorporated into knowledge-based systems
has to be verified for enhancing systems’ reliability. Weather lore knowledge exists in different
forms as identified by traditional communities; hence it needs to be tied together for comparison
and validation. The development of a weather lore validation tool that can integrate a framework
for acquiring weather data and methods of representing the weather lore in verifiable forms can
be a significant step in the validation of weather lore against actual weather records using
conventional weather-observing instruments. The success of validating weather lore could
stimulate the opportunity for integrating acceptable weather lore with modern systems of weather prediction to improve actionable information for decision making that relies on seasonal weather
prediction.
In this study a hybrid method is developed that includes computer vision and fuzzy cognitive
mapping techniques for verifying visual weather lore. The verification tool was designed with
forecasting based on mimicking visual perception, and fuzzy thinking based on the cognitive
knowledge of humans. The method provides meaning to humanly perceivable sky objects so that
computers can understand, interpret, and approximate visual weather outcomes.
Questionnaires were administered in two case study locations (KwaZulu-Natal province in South
Africa, and Taita-Taveta County in Kenya), between the months of March and July 2015. The
two case studies were conducted by interviewing respondents on how visual astronomical and
meteorological weather concepts cause weather outcomes. The two case studies were used to
identify causal effects of visual astronomical and meteorological objects to weather conditions.
This was followed by finding variations and comparisons, between the visual weather lore
knowledge in the two case studies. The results from the two case studies were aggregated in
terms of seasonal knowledge. The causal links between visual weather concepts were
investigated using these two case studies; results were compared and aggregated to build up
common knowledge. The joint averages of the majority of responses from the case studies were determined for each set of interacting concepts.
The modelling of the weather lore verification tool consists of input, processing components and
output. The input data to the system are sky image scenes and actual weather observations from
wireless weather sensors. The image recognition component performs three sub-tasks, including:
detection of objects (concepts) from image scenes, extraction of detected objects, and
approximation of the presence of the concepts by comparing extracted objects to ideal objects.
The prediction process involves the use of approximated concepts generated in the recognition
component to simulate scenarios using the knowledge represented in the fuzzy cognitive maps.
The verification component evaluates the variation between the predictions and actual weather
observations to determine prediction errors and accuracy.
To evaluate the tool, daily system simulations were run to predict and record probabilities of
weather outcomes (i.e. rain, heat index/hotness, dry, cold index). Weather observations were
captured periodically using a wireless weather station. This process was repeated several times until there was sufficient data to use for the verification process. To match the range of the
predicted weather outcomes, the actual weather observations (measurement) were transformed
and normalized to a range [0, 1].In the verification process, comparisons were made between the
actual observations and weather outcome prediction values by computing residuals (error values)
from the observations. The error values and the squared error were used to compute the Mean
Squared Error (MSE), and the Root Mean Squared Error (RMSE), for each predicted weather
outcome.
Finally, the validity of the visual weather lore verification model was assessed using data from a
different geographical location. Actual data in the form of daily sky scenes and weather
parameters were acquired from Voi, Kenya, from December 2015 to January 2016.The results on
the use of hybrid techniques for verification of weather lore is expected to provide an incentive
in integrating indigenous knowledge on weather with modern numerical weather prediction
systems for accurate and downscaled weather forecasts
High-level environment representations for mobile robots
In most robotic applications we are faced with the problem of building
a digital representation of the environment that allows the robot to
autonomously complete its tasks. This internal representation can be
used by the robot to plan a motion trajectory for its mobile base
and/or end-effector. For most man-made environments we do not have
a digital representation or it is inaccurate. Thus, the robot must
have the capability of building it autonomously. This is done by
integrating into an internal data structure incoming sensor
measurements. For this purpose, a common solution consists in solving
the Simultaneous Localization and Mapping (SLAM) problem. The map
obtained by solving a SLAM problem is called ``metric'' and it
describes the geometric structure of the environment. A metric map is
typically made up of low-level primitives (like points or
voxels). This means that even though it represents the shape of the
objects in the robot workspace it lacks the information of which
object a surface belongs to. Having an object-level representation of
the environment has the advantage of augmenting the set of possible
tasks that a robot may accomplish. To this end, in this thesis we
focus on two aspects. We propose a formalism to represent in a uniform
manner 3D scenes consisting of different geometric primitives,
including points, lines and planes. Consequently, we derive a local
registration and a global optimization algorithm that can exploit this
representation for robust estimation. Furthermore, we present a
Semantic Mapping system capable of building an \textit{object-based}
map that can be used for complex task planning and execution. Our
system exploits effective reconstruction and recognition techniques
that require no a-priori information about the environment and can be
used under general conditions
Scene analysis and risk estimation for domestic robots, security and smart homes
The evaluation of risk within a scene is a new and emerging area of research. With the advent of smart enabled homes and the continued development and implementation of domestic robotics, the platform for automated risk assessment within the home is now a possibility. The aim of this thesis is to explore a subsection of the problems facing the
detection and quantification of risk in a domestic setting. A Risk Estimation framework is introduced which provides a flexible and context aware platform from which measurable elements of risk can be combined to create a final risk score for a scene. To populate this framework, three elements of measurable risk are proposed and evaluated: Firstly, scene stability, assessing the location and stability of
objects within an environment through the use of physics simulation techniques. Secondly, hazard feature analysis using two specifically designed novel feature descriptors (3D Voxel HOG and the Physics Behaviour Feature) which determine if the objects within a scene have dangerous or risky properties such as blades or points. Finally, environment interaction, which uses human behaviour simulation to predict human reactions to detected risks and highlight areas of a scene most likely to be visited.
Additionally methodologies are introduced to support these concepts including: a simulation prediction framework which reduces the computational cost of physics simulation, a Robust Filter and Complex Adaboost which aim to improve the robustness and training times required for hazard feature classification models. The Human and Group Behaviour Evaluation framework is introduced to provide a platform from which simulation algorithms can be evaluated without the need for extensive ground truth data. Finally the 3D Risk Scenes (3DRS) dataset is introduced, creating a risk specific dataset for the evaluation of future domestic risk analysis methodologies
Perception of Unstructured Environments for Autonomous Off-Road Vehicles
Autonome Fahrzeuge benötigen die Fähigkeit zur Perzeption als eine notwendige Voraussetzung für eine kontrollierbare und sichere Interaktion, um ihre Umgebung wahrzunehmen und zu verstehen.
Perzeption für strukturierte Innen- und Außenumgebungen deckt wirtschaftlich lukrative Bereiche, wie den autonomen Personentransport oder die Industrierobotik ab, während die Perzeption unstrukturierter Umgebungen im Forschungsfeld der Umgebungswahrnehmung stark unterrepräsentiert ist.
Die analysierten unstrukturierten Umgebungen stellen eine besondere Herausforderung dar, da die vorhandenen, natürlichen und gewachsenen Geometrien meist keine homogene Struktur aufweisen und ähnliche Texturen sowie schwer zu trennende Objekte dominieren.
Dies erschwert die Erfassung dieser Umgebungen und deren Interpretation, sodass Perzeptionsmethoden speziell fĂĽr diesen Anwendungsbereich konzipiert und optimiert werden mĂĽssen.
In dieser Dissertation werden neuartige und optimierte Perzeptionsmethoden für unstrukturierte Umgebungen vorgeschlagen und in einer ganzheitlichen, dreistufigen Pipeline für autonome Geländefahrzeuge kombiniert: Low-Level-, Mid-Level- und High-Level-Perzeption.
Die vorgeschlagenen klassischen Methoden und maschinellen Lernmethoden (ML) zur Perzeption bzw.~Wahrnehmung ergänzen sich gegenseitig. Darüber hinaus ermöglicht die Kombination von Perzeptions- und Validierungsmethoden für jede Ebene eine zuverlässige Wahrnehmung der möglicherweise unbekannten Umgebung, wobei lose und eng gekoppelte Validierungsmethoden kombiniert werden, um eine ausreichende, aber flexible Bewertung der vorgeschlagenen Perzeptionsmethoden zu gewährleisten.
Alle Methoden wurden als einzelne Module innerhalb der in dieser Arbeit vorgeschlagenen Perzeptions- und Validierungspipeline entwickelt, und ihre flexible Kombination ermöglicht verschiedene Pipelinedesigns für eine Vielzahl von Geländefahrzeugen und Anwendungsfällen je nach Bedarf.
Low-Level-Perzeption gewährleistet eine eng gekoppelte Konfidenzbewertung für rohe 2D- und 3D-Sensordaten, um Sensorausfälle zu erkennen und eine ausreichende Genauigkeit der Sensordaten zu gewährleisten.
Darüber hinaus werden neuartige Kalibrierungs- und Registrierungsansätze für Multisensorsysteme in der Perzeption vorgestellt, welche lediglich die Struktur der Umgebung nutzen, um die erfassten Sensordaten zu registrieren: ein halbautomatischer Registrierungsansatz zur Registrierung mehrerer 3D~Light Detection and Ranging (LiDAR) Sensoren und ein vertrauensbasiertes Framework, welches verschiedene Registrierungsmethoden kombiniert und die Registrierung verschiedener Sensoren mit unterschiedlichen Messprinzipien ermöglicht. Dabei validiert die Kombination mehrerer Registrierungsmethoden die Registrierungsergebnisse in einer eng gekoppelten Weise.
Mid-Level-Perzeption ermöglicht die 3D-Rekonstruktion unstrukturierter Umgebungen mit zwei Verfahren zur Schätzung der Disparität von Stereobildern: ein klassisches, korrelationsbasiertes Verfahren für Hyperspektralbilder, welches eine begrenzte Menge an Test- und Validierungsdaten erfordert, und ein zweites Verfahren, welches die Disparität aus Graustufenbildern mit neuronalen Faltungsnetzen (CNNs) schätzt. Neuartige Disparitätsfehlermetriken und eine Evaluierungs-Toolbox für die 3D-Rekonstruktion von Stereobildern ergänzen die vorgeschlagenen Methoden zur Disparitätsschätzung aus Stereobildern und ermöglichen deren lose gekoppelte Validierung.
High-Level-Perzeption konzentriert sich auf die Interpretation von einzelnen 3D-Punktwolken zur Befahrbarkeitsanalyse, Objekterkennung und Hindernisvermeidung. Eine Domänentransferanalyse für State-of-the-art-Methoden zur semantischen 3D-Segmentierung liefert Empfehlungen für eine möglichst exakte Segmentierung in neuen Zieldomänen ohne eine Generierung neuer Trainingsdaten. Der vorgestellte Trainingsansatz für 3D-Segmentierungsverfahren mit CNNs kann die benötigte Menge an Trainingsdaten weiter reduzieren. Methoden zur Erklärbarkeit künstlicher Intelligenz vor und nach der Modellierung ermöglichen eine lose gekoppelte Validierung der vorgeschlagenen High-Level-Methoden mit Datensatzbewertung und modellunabhängigen Erklärungen für CNN-Vorhersagen.
Altlastensanierung und Militärlogistik sind die beiden Hauptanwendungsfälle in unstrukturierten Umgebungen, welche in dieser Arbeit behandelt werden.
Diese Anwendungsszenarien zeigen auch, wie die Lücke zwischen der Entwicklung einzelner Methoden und ihrer Integration in die Verarbeitungskette für autonome Geländefahrzeuge mit Lokalisierung, Kartierung, Planung und Steuerung geschlossen werden kann.
Zusammenfassend lässt sich sagen, dass die vorgeschlagene Pipeline flexible Perzeptionslösungen für autonome Geländefahrzeuge bietet und die begleitende Validierung eine exakte und vertrauenswürdige Perzeption unstrukturierter Umgebungen gewährleistet
Tensor Representations for Object Classification and Detection
A key problem in object recognition is finding a suitable object representation.
For historical and computational reasons, vector descriptions that encode particular
statistical properties of the data have been broadly applied. However, employing
tensor representation can describe the interactions of multiple factors
inherent to image formation. One of the most convenient uses for tensors is to represent
complex objects in order to build a discriminative description.
Thus thesis has several main contributions, focusing on visual data detection (e.g. of heads or pedestrians) and classification (e.g. of head or human body orientation) in still images and on machine learning techniques to analyse tensor data. These applications are among the most studied in computer vision and are typically formulated as binary or multi-class classification problems.
The applicative context of this thesis is the video surveillance, where classification and detection tasks
can be very hard, due to the scarce resolution and the noise characterising
sensor data. Therefore, the main goal in that context is to design algorithms that can
characterise different objects of interest, especially when immersed in a cluttered
background and captured at low resolution.
In the different amount of machine learning approaches, the ensemble-of-classifiers demonstrated to reach
excellent classification accuracy, good generalisation ability, and robustness of noisy data. For these
reasons, some approaches in that class have been adopted as basic machine classification
frameworks to build robust classifiers and detectors. Moreover, also
kernel machines has been exploited for classification purposes,
since they represent a natural learning framework for tensors
Proceedings of the 2011 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory
This book is a collection of 15 reviewed technical reports summarizing the presentations at the 2011 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory. The covered topics include image processing, optical signal processing, visual inspection, pattern recognition and classification, human-machine interaction, world and situation modeling, autonomous system localization and mapping, information fusion, and trust propagation in sensor networks
Deep Learning based Vehicle Detection in Aerial Imagery
Der Einsatz von luftgestützten Plattformen, die mit bildgebender Sensorik ausgestattet sind, ist ein wesentlicher Bestandteil von vielen Anwendungen im Bereich der zivilen Sicherheit. Bekannte Anwendungsgebiete umfassen unter anderem die Entdeckung verbotener oder krimineller Aktivitäten, Verkehrsüberwachung, Suche und Rettung, Katastrophenhilfe und Umweltüberwachung. Aufgrund der großen Menge zu verarbeitender Daten und der daraus resultierenden kognitiven Überbelastung ist jedoch eine Analyse der Luftbilddaten ausschließlich durch menschliche Auswerter in der Praxis nicht anwendbar. Zur Unterstützung der menschlichen Auswerter kommen daher in der Regel automatische Bild- und Videoverarbeitungsalgorithmen zum Einsatz. Eine zentrale Aufgabe bildet dabei eine zuverlässige Detektion relevanter Objekte im Sichtfeld der Kamera, bevor eine Interpretation der gegebenen Szene stattfinden kann. Die geringe Bodenauflösung aufgrund der großen Distanz zwischen Kamera und Erde macht die Objektdetektion in Luftbilddaten zu einer herausfordernden Aufgabe, welche durch Bewegungsunschärfe, Verdeckungen und Schattenwurf zusätzlich erschwert wird. Obwohl in der Literatur eine Vielzahl konventioneller Ansätze zur Detektion von Objekten in Luftbilddaten existiert, ist die Detektionsgenauigkeit durch die Repräsentationsfähigkeit der verwendeten manuell entworfenen Merkmale beschränkt.
Im Rahmen dieser Arbeit wird ein neuer Deep-Learning basierter Ansatz zur Detektion von Objekten in Luftbilddaten präsentiert. Der Fokus der Arbeit liegt dabei auf der Detektion von Fahrzeugen in Luftbilddaten, die senkrecht von oben aufgenommen wurden. Grundlage des entwickelten Ansatzes bildet der Faster R-CNN Detektor, der im Vergleich zu anderen Deep-Learning basierten Detektionsverfahren eine höhere Detektionsgenauigkeit besitzt. Da Faster R-CNN wie auch die anderen Deep-Learning basierten Detektionsverfahren auf Benchmark Datensätzen optimiert wurden, werden in einem ersten Schritt notwendige Anpassungen an die Eigenschaften der Luftbilddaten, wie die geringen Abmessungen der zu detektierenden Fahrzeuge, systematisch untersucht und daraus resultierende Probleme identifiziert. Im Hinblick auf reale Anwendungen sind hier vor allem die hohe Anzahl fehlerhafter Detektionen durch fahrzeugähnliche Strukturen und die deutlich erhöhte Laufzeit problematisch. Zur Reduktion der fehlerhaften Detektionen werden zwei neue Ansätze vorgeschlagen. Beide Ansätze verfolgen dabei das Ziel, die verwendete Merkmalsrepräsentation durch zusätzliche Kontextinformationen zu verbessern. Der erste Ansatz verfeinert die räumlichen Kontextinformationen durch eine Kombination der Merkmale von frühen und tiefen Schichten der zugrundeliegenden CNN Architektur, so dass feine und grobe Strukturen besser repräsentiert werden. Der zweite Ansatz macht Gebrauch von semantischer Segmentierung um den semantischen Informationsgehalt zu erhöhen. Hierzu werden zwei verschiedene Varianten zur Integration der semantischen Segmentierung in das Detektionsverfahren realisiert: zum einen die Verwendung der semantischen Segmentierungsergebnisse zur Filterung von unwahrscheinlichen Detektionen und zum anderen explizit durch Verschmelzung der CNN Architekturen zur Detektion und Segmentierung. Sowohl durch die Verfeinerung der räumlichen Kontextinformationen als auch durch die Integration der semantischen Kontextinformationen wird die Anzahl der fehlerhaften Detektionen deutlich reduziert und somit die Detektionsgenauigkeit erhöht. Insbesondere der starke Rückgang von fehlerhaften Detektionen in unwahrscheinlichen Bildregionen, wie zum Beispiel auf Gebäuden, zeigt die erhöhte Robustheit der gelernten Merkmalsrepräsentationen. Zur Reduktion der Laufzeit werden im Rahmen der Arbeit zwei alternative Strategien verfolgt. Die erste Strategie ist das Ersetzen der zur Merkmalsextraktion standardmäßig verwendeten CNN Architektur mit einer laufzeitoptimierten CNN Architektur unter Berücksichtigung der Eigenschaften der Luftbilddaten, während die zweite Strategie ein neues Modul zur Reduktion des Suchraumes umfasst. Mit Hilfe der vorgeschlagenen Strategien wird die Gesamtlaufzeit sowie die Laufzeit für jede Komponente des Detektionsverfahrens deutlich reduziert. Durch Kombination der vorgeschlagenen Ansätze kann sowohl die Detektionsgenauigkeit als auch die Laufzeit im Vergleich zur Faster R-CNN Baseline signifikant verbessert werden. Repräsentative Ansätze zur Fahrzeugdetektion in Luftbilddaten aus der Literatur werden quantitativ und qualitativ auf verschiedenen Datensätzen übertroffen. Des Weiteren wird die Generalisierbarkeit des entworfenen Ansatzes auf ungesehenen Bildern von weiteren Luftbilddatensätzen mit abweichenden Eigenschaften demonstriert
Deliverable D1.1 State of the art and requirements analysis for hypervideo
This deliverable presents a state-of-art and requirements analysis report for hypervideo authored as part of the WP1 of the LinkedTV project. Initially, we present some use-case (viewers) scenarios in the LinkedTV project and through the analysis of the distinctive needs and demands of each scenario we point out the technical requirements from a user-side perspective. Subsequently we study methods for the automatic and semi-automatic decomposition of the audiovisual content in order to effectively support the annotation process. Considering that the multimedia content comprises of different types of information, i.e., visual, textual and audio, we report various methods for the analysis of these three different streams. Finally we present various annotation tools which could integrate the developed analysis results so as to effectively support users (video producers) in the semi-automatic linking of hypervideo content, and based on them we report on the initial progress in building the LinkedTV annotation tool. For each one of the different classes of techniques being discussed in the deliverable we present the evaluation results from the application of one such method of the literature to a dataset well-suited to the needs of the LinkedTV project, and we indicate the future technical requirements that should be addressed in order to achieve higher levels of performance (e.g., in terms of accuracy and time-efficiency), as necessary
Large Scale Pattern Detection in Videos and Images from the Wild
PhDPattern detection is a well-studied area of computer vision, but still current methods are
unstable in images of poor quality. This thesis describes improvements over contemporary
methods in the fast detection of unseen patterns in a large corpus of videos that vary
tremendously in colour and texture definition, captured “in the wild” by mobile devices
and surveillance cameras.
We focus on three key areas of this broad subject;
First, we identify consistency weaknesses in existing techniques of processing an image
and it’s horizontally reflected (mirror) image. This is important in police investigations
where subjects change their appearance to try to avoid recognition, and we propose that
invariance to horizontal reflection should be more widely considered in image description
and recognition tasks too. We observe online Deep Learning system behaviours in
this respect, and provide a comprehensive assessment of 10 popular low level feature
detectors.
Second, we develop simple and fast algorithms that combine to provide memory- and
processing-efficient feature matching. These involve static scene elimination in the presence
of noise and on-screen time indicators, a blur-sensitive feature detection that finds
a greater number of corresponding features in images of varying sharpness, and a combinatorial
texture and colour feature matching algorithm that matches features when
either attribute may be poorly defined. A comprehensive evaluation is given, showing
some improvements over existing feature correspondence methods.
Finally, we study random decision forests for pattern detection. A new method of
indexing patterns in video sequences is devised and evaluated. We automatically label
positive and negative image training data, reducing a task of unsupervised learning to
one of supervised learning, and devise a node split function that is invariant to mirror
reflection and rotation through 90 degree angles. A high dimensional vote accumulator
encodes the hypothesis support, yielding implicit back-projection for pattern detection.European Union’s Seventh Framework Programme, specific
topic “framework and tools for (semi-) automated exploitation of massive amounts of digital data
for forensic purposes”, under grant agreement number 607480 (LASIE IP project)
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