2,345 research outputs found
Model Checking Spatial Logics for Closure Spaces
Spatial aspects of computation are becoming increasingly relevant in Computer
Science, especially in the field of collective adaptive systems and when
dealing with systems distributed in physical space. Traditional formal
verification techniques are well suited to analyse the temporal evolution of
programs; however, properties of space are typically not taken into account
explicitly. We present a topology-based approach to formal verification of
spatial properties depending upon physical space. We define an appropriate
logic, stemming from the tradition of topological interpretations of modal
logics, dating back to earlier logicians such as Tarski, where modalities
describe neighbourhood. We lift the topological definitions to the more general
setting of closure spaces, also encompassing discrete, graph-based structures.
We extend the framework with a spatial surrounded operator, a propagation
operator and with some collective operators. The latter are interpreted over
arbitrary sets of points instead of individual points in space. We define
efficient model checking procedures, both for the individual and the collective
spatial fragments of the logic and provide a proof-of-concept tool
CTex - an adaptive unsupervised segmentation algorithm based on color-texture coherence
This paper presents the development of an unsupervised image segmentation framework (referred to as CTex) that is based on the adaptive inclusion of color and texture in the process of data partition. An important contribution of this work consists of a new formulation for the extraction of color features that evaluates the input image in a multispace color representation. To achieve this, we have used the opponent characteristics of the RGB and YIQ color spaces where the key component was the inclusion of the self organizing map (SOM) network in the computation of the dominant colors and estimation of the optimal number of clusters in the image. The texture features are computed using a multichannel texture decomposition scheme based on Gabor filtering. The major contribution of this work resides in the adaptive integration of the color and texture features in a compound mathematical descriptor with the aim of identifying the homogenous regions in the image. This integration is performed by a novel adaptive clustering algorithm that enforces the spatial continuity during the data assignment process. A comprehensive qualitative and quantitative performance evaluation has been carried out and the experimental results indicate that the proposed technique is accurate in capturing the color and texture characteristics when applied to complex natural images
Adaptive visual sampling
PhDVarious visual tasks may be analysed in the context of sampling from the visual field. In visual
psychophysics, human visual sampling strategies have often been shown at a high-level to
be driven by various information and resource related factors such as the limited capacity of
the human cognitive system, the quality of information gathered, its relevance in context and
the associated efficiency of recovering it. At a lower-level, we interpret many computer vision
tasks to be rooted in similar notions of contextually-relevant, dynamic sampling strategies
which are geared towards the filtering of pixel samples to perform reliable object association. In
the context of object tracking, the reliability of such endeavours is fundamentally rooted in the
continuing relevance of object models used for such filtering, a requirement complicated by realworld
conditions such as dynamic lighting that inconveniently and frequently cause their rapid
obsolescence. In the context of recognition, performance can be hindered by the lack of learned
context-dependent strategies that satisfactorily filter out samples that are irrelevant or blunt the
potency of models used for discrimination. In this thesis we interpret the problems of visual
tracking and recognition in terms of dynamic spatial and featural sampling strategies and, in this
vein, present three frameworks that build on previous methods to provide a more flexible and
effective approach.
Firstly, we propose an adaptive spatial sampling strategy framework to maintain statistical object
models for real-time robust tracking under changing lighting conditions. We employ colour
features in experiments to demonstrate its effectiveness. The framework consists of five parts:
(a) Gaussian mixture models for semi-parametric modelling of the colour distributions of multicolour
objects; (b) a constructive algorithm that uses cross-validation for automatically determining
the number of components for a Gaussian mixture given a sample set of object colours; (c) a
sampling strategy for performing fast tracking using colour models; (d) a Bayesian formulation
enabling models of object and the environment to be employed together in filtering samples by
discrimination; and (e) a selectively-adaptive mechanism to enable colour models to cope with
changing conditions and permit more robust tracking.
Secondly, we extend the concept to an adaptive spatial and featural sampling strategy to deal
with very difficult conditions such as small target objects in cluttered environments undergoing
severe lighting fluctuations and extreme occlusions. This builds on previous work on dynamic
feature selection during tracking by reducing redundancy in features selected at each stage as
well as more naturally balancing short-term and long-term evidence, the latter to facilitate model
rigidity under sharp, temporary changes such as occlusion whilst permitting model flexibility
under slower, long-term changes such as varying lighting conditions. This framework consists of
two parts: (a) Attribute-based Feature Ranking (AFR) which combines two attribute measures;
discriminability and independence to other features; and (b) Multiple Selectively-adaptive Feature
Models (MSFM) which involves maintaining a dynamic feature reference of target object
appearance. We call this framework Adaptive Multi-feature Association (AMA). Finally, we present an adaptive spatial and featural sampling strategy that extends established
Local Binary Pattern (LBP) methods and overcomes many severe limitations of the traditional
approach such as limited spatial support, restricted sample sets and ad hoc joint and disjoint statistical
distributions that may fail to capture important structure. Our framework enables more
compact, descriptive LBP type models to be constructed which may be employed in conjunction
with many existing LBP techniques to improve their performance without modification. The
framework consists of two parts: (a) a new LBP-type model known as Multiscale Selected Local
Binary Features (MSLBF); and (b) a novel binary feature selection algorithm called Binary Histogram
Intersection Minimisation (BHIM) which is shown to be more powerful than established
methods used for binary feature selection such as Conditional Mutual Information Maximisation
(CMIM) and AdaBoost
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Fast embedding for image classification & retrieval and its application to the hostel industry
This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonContent-based image classification and retrieval are the automatic processes of taking
an unseen image input and extracting its features representing the input image. Then,
for the classification task, this mathematically measured input is categorized according
to established criteria in the server and consequently shows the output as a result. On
the other hand, for the retrieval task, the extracted features of an unseen query image
are sent to the server to search for the most visually similar images to a given image
and retrieve these images as a result. Despite image features could be represented
by classical features, artificial intelligence-based features, Convolutional Neural
Networks (CNN) to be precise, have become powerful tools in the field. Nonetheless,
the high dimensional CNN features have been a challenge in particular for applications
on mobile or Internet of Things devices. Therefore, in this thesis, several fast
embeddings are explored and proposed to overcome the constraints of low memory,
bandwidth, and power. Furthermore, the first hostel image database is created with
three datasets, hostel image dataset containing 13,908 interior and exterior images of
hostels across the world, and Hostels-900 dataset and Hostels-2K dataset containing
972 images and 2,380 images, respectively, of 20 London hostel buildings. The results
demonstrate that the proposed fast embeddings such as the application of GHM-Rand
operator, GHM-Fix operator, and binary feature vectors are able to outperform or give
competitive results to those state-of-the-art methods with a lot less computational
resource. Additionally, the findings from a ten-year literature review of CBIR study in
the tourism industry could picturize the relevant research activities in the past decade
which are not only beneficial to the hostel industry or tourism sector but also to the
computer science and engineering research communities for the potential real-life
applications of the existing and developing technologies in the field
Multi-agent evolutionary systems for the generation of complex virtual worlds
Modern films, games and virtual reality applications are dependent on
convincing computer graphics. Highly complex models are a requirement for the
successful delivery of many scenes and environments. While workflows such as
rendering, compositing and animation have been streamlined to accommodate
increasing demands, modelling complex models is still a laborious task. This
paper introduces the computational benefits of an Interactive Genetic Algorithm
(IGA) to computer graphics modelling while compensating the effects of user
fatigue, a common issue with Interactive Evolutionary Computation. An
intelligent agent is used in conjunction with an IGA that offers the potential
to reduce the effects of user fatigue by learning from the choices made by the
human designer and directing the search accordingly. This workflow accelerates
the layout and distribution of basic elements to form complex models. It
captures the designer's intent through interaction, and encourages playful
discovery
Measurment of spatial orientation using a biologically plausible gradient model
A Thesis submitted for the degree of Doctor of Philosophy
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