Advanced Image Acquisition, Processing Techniques and Applications

"Advanced Image Acquisition, Processing Techniques and Applications" is the first book of a series that provides image processing principles and practical software implementation on a broad range of applications. The book integrates material from leading researchers on Applied Digital Image Acquisition and Processing. An important feature of the book is its emphasis on software tools and scientific computing in order to enhance results and arrive at problem solution

Image Processing and Pattern Recognition Applied to Soil Structure

This thesis represents a collaborative research between the Department of Electronics & Electrical Engineering and the Department of Civil Engineering, University of Glasgow. The project was initially aimed at development of some theories and techniques of image processing and pattern recognition for the study of soil microstructures. More specifically, the aim was to study the shapes, orientations, and arrangements of soil particles and voids (i.e. pores): these three are very important properties, which are used both for description, recognition and classification of soils, and also for studying the relationships between the soil structures and physical, chemical, geological, geographical, and environmental changes. The work presented here was based principally on a need for analysing the structure of soil as recorded in two-dimensional images which might be conventional photographs, optical micrographs, or electron-micrographs. In this thesis, first a brief review of image processing and pattern recognition and their previous application in the study of soil microstructures is given. Then a convex hull based shape description and classification for soil particles is presented. A new algorithm, SPCH, is proposed for finding the convex hull of either a binary object or a cluster of points in a plane. This algorithm is efficient and reliable. Features of pattern vectors for shape description and classification are obtained from the convex hull and the object. These features are invariant with respect to coordinate rotation, translation, and scaling. The objects can then be classified by any standard feature-space method: here minimum-distance classification was used. Next the orientation analysis of soil particles is described. A new method, Directed Vein, is proposed for the analysis. Another three methods: Convex Hull, Principal Components, and Moments, are also presented. Comparison of the four methods shows that the Directed Vein method appears the fastest; but it also has the special property of estimating an 'internal preferred orientation' whereas the other methods estimate an 'elongation direction'. Fourth, the roundness/sharpness analysis of soil particles is presented. Three new algorithms, referred to as the Centre, Gradient Centre, and Radius methods, all based on the Circular Hough Transform, are proposed. Two traditional Circular Hough Transform algorithms are presented as well. The three new methods were successfully applied to the measurement of the roundness (sharpness of comers) of two-dimensional particles. The five methods were compared from the points of view of memory requirement, speed, and accuracy; and the Radius method appears to be the best for the special topic of sharpness/roundness analysis. Finally the analysis and classification of aggregates of objects is introduced. A new method. Extended Linear Hough Transform, is proposed. In this method, the orientations and locations of the objects are mapped into extended Hough space. The arrangements of the objects within an aggregate are then determined by analysing the data distributions in this space. The aggregates can then be classified using a tree classifier. Taken together, the methods developed or tested here provide a useful toolkit for analysing the shapes, orientation, and aggregation of particles such as those seen in two-dimensional images of soil structure at various scales

Exploring Engineering Applications of Visual Analytics in Virtual Reality

Recent advancements and technological breakthroughs in the development of so-called immersive interfaces, such as augmented (AR), mixed (MR), and virtual reality (VR), coupled with the growing mass-market adoption of such devices has started to attract attention from academia and industry alike. Out of these technologies, VR offers the most mature option in terms of both hardware and software, as well as the best available range of different off-the-shelf offerings. VR is a term interchangeably used to denote both head-mounted displays (HMDs) and fully immersive, bespoke 3D environments which these devices transport their users to. With modern devices, developers can leverage a range of different interaction modalities, including visual, audio, and even haptic feedback, in the creation of these virtual worlds. With such a rich interaction space it is thus natural to think of VR as a well-suited environment for interactive visualisation and analytical reasoning of complex multidimensional data. Research in \textit{visual analytics} (VA) combines these two themes, spanning the last one and a half decades, and has revealed a number of research findings. This includes a range of new advanced and effective visualisation and analysis tools for even more complex, more noisy and larger data sets. Furthermore, the extension of this research and the use of immersive interfaces to facilitate visual analytics has spun-off a new field of research: \textit{immersive analytics} (IA). Immersive analytics leverages the potential bestowed by immersive interfaces to aid the user in swift and effective data analysis. Some of the most promising application domains of such immersive interfaces in the industry are various branches of engineering, including aerospace design and in civil engineering. The range of potential applications is vast and growing as new stakeholders are adopting these immersive tools. However, the use of these technologies brings its own challenges. One such difficulty is the design of appropriate interaction techniques. There is no optimal choice, instead such a choice varies depending on available hardware, the user’s prior experience, their task at hand, and the nature of the dataset. To this end, my PhD work has focused on designing and analysing various interactive, VR-based immersive systems for engineering visual analytics. One of the key elements of such an immersive system is the selection of an adequate interaction method. In a series of both qualitative and quantitative studies, I have explored the potential of various interaction techniques that can be used to support the user in swift and effective data analysis. Here, I have investigated the feasibility of using techniques such as hand-held controllers, gaze-tracking and hand-tracking input methods used solo or in combination in various challenging use cases and scenarios. For instance, I developed and verified the usability and effectiveness of the AeroVR system for aerospace design in VR. This research has allowed me to trim the very large design space of such systems that have been not sufficiently explored thus far. Moreover, building on top of this work, I have designed, developed, and tested a system for digital twin assessment in aerospace that coupled gaze-tracking and hand-tracking, achieved via an additional sensor attached to the front of the VR headset, with no need for the user to hold a controller. The analysis of the results obtained from a qualitative study with domain experts allowed me to distill and propose design implications when developing similar systems. Furthermore, I worked towards designing an effective VR-based visualisation of complex, multidimensional abstract datasets. Here, I developed and evaluated the immersive version of the well-known Parallel Coordinates Plots (IPCP) visualisation technique. The results of the series of qualitative user studies allowed me to obtain a list of design suggestions for IPCP, as well as provide tentative evidence that the IPCP can be an effective tool for multidimensional data analysis. Lastly, I also worked on the design, development, and verification of the system allowing its users to capture information in the context of conducting engineering surveys in VR. Furthermore, conducting a meaningful evaluation of immersive analytics interfaces remains an open problem. It is difficult and often not feasible to use traditional A/B comparisons in controlled experiments as the aim of immersive analytics is to provide its users with new insights into their data rather than focusing on more quantifying factors. To this end, I developed a generative process for synthesising clustered datasets for VR analytics experiments that can be used in the process of interface evaluation. I further validated this approach by designing and carrying out two user studies. The statistical analysis of the gathered data revealed that this generative process for synthesising clustered datasets did indeed result in datasets that can be used in experiments without the datasets themselves being the dominant contributor of the variability between conditions.Engineering and Physical Sciences Research Council (EPSRC-1788814); Trinity Hall and Cambridge Commonwealth, European & International Trust; Cambridge Philosophical Societ

A hierarchical active binocular robot vision architecture for scene exploration and object appearance learning

This thesis presents an investigation of a computational model of hierarchical visual behaviours within an active binocular robot vision architecture. The robot vision system is able to localise multiple instances of the same object class, while simultaneously maintaining vergence and directing its gaze to attend and recognise objects within cluttered, complex scenes. This is achieved by implementing all image analysis in an egocentric symbolic space without creating explicit pixel-space maps and without the need for calibration or other knowledge of the camera geometry. One of the important aspects of the active binocular vision paradigm requires that visual features in both camera eyes must be bound together in order to drive visual search to saccade, locate and recognise putative objects or salient locations in the robot's field of view. The system structure is based on the “attentional spotlight” metaphor of biological systems and a collection of abstract and reactive visual behaviours arranged in a hierarchical structure. Several studies have shown that the human brain represents and learns objects for recognition by snapshots of 2-dimensional views of the imaged scene that happens to contain the object of interest during active interaction (exploration) of the environment. Likewise, psychophysical findings specify that the primate’s visual cortex represents common everyday objects by a hierarchical structure of their parts or sub-features and, consequently, recognise by simple but imperfect 2D view object part approximations. This thesis incorporates the above observations into an active visual learning behaviour in the hierarchical active binocular robot vision architecture. By actively exploring the object viewing sphere (as higher mammals do), the robot vision system automatically synthesises and creates its own part-based object representation from multiple observations while a human teacher indicates the object and supplies a classification name. Its is proposed to adopt the computational concepts of a visual learning exploration mechanism that controls the accumulation of visual evidence and directs attention towards the spatial salient object parts. The behavioural structure of the binocular robot vision architecture is loosely modelled by a WHAT and WHERE visual streams. The WHERE stream maintains and binds spatial attention on the object part coordinates that egocentrically characterises the location of the object of interest and extracts spatio-temporal properties of feature coordinates and descriptors. The WHAT stream either determines the identity of an object or triggers a learning behaviour that stores view-invariant feature descriptions of the object part. Therefore, the robot vision is capable to perform a collection of different specific visual tasks such as vergence, detection, discrimination, recognition localisation and multiple same-instance identification. This classification of tasks enables the robot vision system to execute and fulfil specified high-level tasks, e.g. autonomous scene exploration and active object appearance learning

Tumour grading and discrimination based on class assignment and quantitative texture analysis techniques

Medical imaging represents the utilisation of technology in biology for the purpose of noninvasively revealing the internal structure of the organs of the human body. It is a way to improve the quality of the patient's life through a more precise and rapid diagnosis, and with limited side-effects, leading to an effective overall treatment procedure. The main objective of this thesis is to propose novel tumour discrimination techniques that cover both micro and macro-scale textures encountered in computed tomography (CI') and digital microscopy (DM) modalities, respectively. Image texture can provide significant information on the (ab)normality of tissue, and this thesis expands this idea to tumour texture grading and classification. The fractal dimension (FO) as a texture measure was applied to contrast enhanced CT lung tumour images in an aim to improve tumour grading accuracy from conventional CI' modality, and quantitative performance analysis showed an accuracy of 83.30% in distinguishing between advanced (aggressive) and early stage (non-aggressive) malignant tumours. A different approach was adopted for subtype discrimination of brain tumour OM images via a set of statistical and model-based texture analysis algorithms. The combined Gaussian Markov random field and run-length matrix texture measures outperformed all other combinations, achieving an overall class assignment classification accuracy of 92.50%. Also two new histopathological multi resolution approaches based on applying the FO as the best bases selection for discrete wavelet packet transform, and when fused with the Gabor filters' energy output improved the accuracy to 91.25% and 95.00%, respectively. While noise is quite common in all medical imaging modalities, the impact of noise on the applied texture measures was assessed as well. The developed lung and brain texture analysis techniques can improve the physician's ability to detect and analyse pathologies leading for a more reliable diagnosis and treatment of disease

Structural modelling and robustness analysis of complex metabolic networks and signal transduction cascades

The dissertation covers the topic of structural robustness of metabolic networks on the basis of the concept of elementary flux modes (EFMs). It is shown that the number of EFMs does not reflect the topology of a network sufficiently. Thus, new methods are developed to determine the structural robustness of metabolic networks. These methods are based on systematic in-silico knockouts and the subsequent calculation of dropped out EFMs. Thereby, together with single knockouts also double and multiple knockouts can be used. After evaluation of these methods they are applied to metabolic networks of human erythrocyte and hepatocyte as well as to a metabolic network of Escherichia coli (E. coli). It is found that the erythrocyte has the lowest structural robustness, followed by the hepatocyte and E. coli. These results coincide very well with the circumstance that human erythrocyte and hepatocyte and E. coli are able to adapt to conditions with increasing diversity. In a further part of the dissertation the concept of EFMs is expanded to signal transduction pathways consisting of kinase cascades. The concept of EFMs is based on the steady-state condition for metabolic pathways. It is shown that under certain circumstances this steady-state condition also holds for signalling cascades. Furthermore, it is shown that it is possible to deduce minimal conditions for signal transduction without knowledge about the kinetics involved. On the basis of these assumptions it is possible to calculate EFMs for signalling cascades. But due to the fact that these EFMs do no longer just have mass flux but also information flux, they are now called elementary signalling modes (ESMs).Die Dissertation behandelt die strukturelle Robustheit von metabolischen Netzwerken auf der Basis des Konzepts der elementaren Flussmoden (EFMen). Es wird gezeigt, dass die Anzahl der EFMen die Topologie eines metabolischen Netzes nicht ausreichend widerspiegelt. Darauf aufbauend werden neue Methoden entwickelt, um die strukturelle Robustheit metabolischer Netze zu bestimmen. Diese Methoden beruhen auf systematischen in-silico-Knockouts und der anschließenden Bestimmung des Anteils an weggefallenen EFMen. Dabei können neben Einfach-Knockouts auch Doppel- oder Mehrfach-Knockouts verwendet werden. Nach der Evaluierung werden diese Methoden auf metabolische Netzwerke des menschlichen Erythrozyten und Hepatozyten, sowie des Bakteriums Escherichia coli (E. coli) angewendet. Es zeigt sich, dass der Erythrozyt die im Vergleich geringste strukturelle Robustheit besitzt, gefolgt vom Hepatozyten und E. coli. Diese Ergebnisse stimmen sehr gut mit der Beobachtung überein, dass sich die menschlichen Erythrozyten und Hepatozyten, sowie E. coli an zunehmend verschiedene Bedingungen anpassen können. In einem weiteren Teil der Dissertation wird das Konzept der EFMen auf Signaltransduktionswege bestehend aus Kinase-Kaskaden erweitert. Das Konzept der EFMen beruht auf der Annahme eines quasi-stationären Zustands für metabolische Netzwerke. Es wird gezeigt, dass dieser quasi-stationäre Zustand unter bestimmten Bedingungen auch in Signal-Kaskaden angenommen werden kann. Weiterhin wird gezeigt, dass man ohne Kenntnis der beteiligten Kinetiken Minimalbedingungen für die Signalweiterleitung ableiten kann. Auf Basis dieser Annahmen lassen sich für Signal-Kaskaden EFMen berechnen. Aber aufgrund der Tatsache, dass sie nicht mehr nur Masse-, sondern auch Informationsfluss beschreiben, werden sie nun als elementare Signalmoden (ESMen) bezeichnet