On Martian Surface Exploration: Development of Automated 3D Reconstruction and Super-Resolution Restoration Techniques for Mars Orbital Images

Very high spatial resolution imaging and topographic (3D) data play an important role in modern Mars science research and engineering applications. This work describes a set of image processing and machine learning methods to produce the “best possible” high-resolution and high-quality 3D and imaging products from existing Mars orbital imaging datasets. The research work is described in nine chapters of which seven are based on separate published journal papers. These include a) a hybrid photogrammetric processing chain that combines the advantages of different stereo matching algorithms to compute stereo disparity with optimal completeness, fine-scale details, and minimised matching artefacts; b) image and 3D co-registration methods that correct a target image and/or 3D data to a reference image and/or 3D data to achieve robust cross-instrument multi-resolution 3D and image co-alignment; c) a deep learning network and processing chain to estimate pixel-scale surface topography from single-view imagery that outperforms traditional photogrammetric methods in terms of product quality and processing speed; d) a deep learning-based single-image super-resolution restoration (SRR) method to enhance the quality and effective resolution of Mars orbital imagery; e) a subpixel-scale 3D processing system using a combination of photogrammetric 3D reconstruction, SRR, and photoclinometric 3D refinement; and f) an optimised subpixel-scale 3D processing system using coupled deep learning based single-view SRR and deep learning based 3D estimation to derive the best possible (in terms of visual quality, effective resolution, and accuracy) 3D products out of present epoch Mars orbital images. The resultant 3D imaging products from the above listed new developments are qualitatively and quantitatively evaluated either in comparison with products from the official NASA planetary data system (PDS) and/or ESA planetary science archive (PSA) releases, and/or in comparison with products generated with different open-source systems. Examples of the scientific application of these novel 3D imaging products are discussed

Development and application of fluorescence lifetime imaging and super-resolution microscopy

This PhD thesis reports the development and application of fluorescence imaging technologies for studying biological processes on spatial scales below the diffraction limit. Two strategies were addressed: firstly fluorescence lifetime imaging (FLIM) to study molecular processes, e.g. using Förster resonance energy transfer (FRET) to read out protein interactions, and secondly direct imaging of nanostructure using super-resolution microscopy (SRM). For quantitative FRET readouts, the development and characterisation of an automated multiwell plate FLIM microscope for high content analysis (HCA) is described. Open source software was developed for the data acquisition and analysis, and approaches to improve the performance of time-gated imaging for FLIM were evaluated including different methods to despeckle the laser illumination and testing of an enhanced detector. This instrument was evaluated using standard fluorescent dye samples and cells expressing fluorescent protein-based FRET constructs. It was applied to an assay of live cells expressing a FRET biosensor and to FRET readouts of aggregation of a membrane receptor (DDR1) in fixed cells. A novel instrument, combining structured illumination microscopy (SIM) with FLIM, was developed to explore the combination of SRM and FLIM-FRET readouts. This enabled the simultaneous mapping of molecular readouts with FLIM and super-resolved imaging. The SIM+FLIM system was applied to image collagen-stimulated DDR1 aggregation in cells, to image DNA structures during the cell cycle and to explore interactions between cell organelles. A novel SRM approach based on a stimulated emission of depletion (STED) microscope incorporating a spatial light modulator (SLM) was developed to provide straightforward robust alignment with collinear excitation/depletion beams, aberration correction, an extended field of view and multiple beam scanning for faster STED image acquisition. The performance of easySLM-STED was evaluated by imaging bead samples, labelled vimentin in Vero cells and the synaptonemal complex in homologs of C. elegans germlines.Open Acces

3D computational modeling and perceptual analysis of kinetic depth effects

Humans have the ability to perceive kinetic depth effects, i.e., to perceived 3D shapes from 2D projections of rotating 3D objects. This process is based on a variety of visual cues such as lighting and shading effects. However, when such cues are weak or missing, perception can become faulty, as demonstrated by the famous silhouette illusion example of the spinning dancer. Inspired by this, we establish objective and subjective evaluation models of rotated 3D objects by taking their projected 2D images as input. We investigate five different cues: ambient luminance, shading, rotation speed, perspective, and color difference between the objects and background. In the objective evaluation model, we first apply 3D reconstruction algorithms to obtain an objective reconstruction quality metric, and then use quadratic stepwise regression analysis to determine weights of depth cues to represent the reconstruction quality. In the subjective evaluation model, we use a comprehensive user study to reveal correlations with reaction time and accuracy, rotation speed, and perspective. The two evaluation models are generally consistent, and potentially of benefit to inter-disciplinary research into visual perception and 3D reconstruction

Finding Objects of Interest in Images using Saliency and Superpixels

The ability to automatically find objects of interest in images is useful in the areas of compression, indexing and retrieval, re-targeting, and so on. There are two classes of such algorithms – those that find any object of interest with no prior knowledge, independent of the task, and those that find specific objects of interest known a priori. The former class of algorithms tries to detect objects in images that stand-out, i.e. are salient, by virtue of being different from the rest of the image and consequently capture our attention. The detection is generic in this case as there is no specific object we are trying to locate. The latter class of algorithms detects specific known objects of interest and often requires training using features extracted from known examples. In this thesis we address various aspects of finding objects of interest under the topics of saliency detection and object detection. We present two saliency detection algorithms that rely on the principle of center-surround contrast. These two algorithms are shown to be superior to several state-of-the-art techniques in terms of precision and recall measures with respect to a ground truth. They output full-resolution saliency maps, are simpler to implement, and are computationally more efficient than most existing algorithms. We further establish the relevance of our saliency detection algorithms by using them for the known applications of object segmentation and image re-targeting. We first present three different techniques for salient object segmentation using our saliency maps that are based on clustering, graph-cuts, and geodesic distance based labeling. We then demonstrate the use of our saliency maps for a popular technique of content-aware image resizing and compare the result with that of existing methods. Our saliency maps prove to be a much more effective replacement for conventional gradient maps for providing automatic content-awareness. Just as it is important to find regions of interest in images, it is also important to find interesting images within a large collection of images. We therefore extend the notion of saliency detection in images to image databases. We propose an algorithm for finding salient images in a database. Apart from finding such images we also present two novel techniques for creating visually appealing summaries in the form of collages and mosaics. Finally, we address the problem of finding specific known objects of interest in images. Specifically, we deal with the feature extraction step that is a pre-requisite for any technique in this domain. In this context, we first present a superpixel segmentation algorithm that outperforms previous algorithms in terms quantitative measures of under-segmentation error and boundary recall. Our superpixel segmentation algorithm also offers several other advantages over existing algorithms like compactness, uniform size, control on the number of superpixels, and computational efficiency. We prove the effectiveness of our superpixels by deploying them in existing algorithms, specifically, an object class detection technique and a graph based algorithm, and improving their performance. We also present the result of using our superpixels in a technique for detecting mitochondria in noisy medical images

Layerings in the nucleus of comet 67P/Churyumov-Gerasimenko

The Rosetta mission delivered images of comet 67P's nucleus at unprecedented resolution which indicate the presence of a global layering system. By merging techniques of structural geology, statistical image processing, and solar system science, this thesis aims to contribute to the understanding of the formation of the layerings, and thus of cometary nuclei as a whole. I describe the two distinctive approaches to studying the layerings' orientation on comet 67P's nucleus. First, I mapped layering-related linear features on a 3D shape model of the nucleus, onto which I projected high-res OSIRIS images. I selected only lineaments of substantial curvature, and used a plane-fitting algorithm to find the normals to the layering planes represented by these lineaments. I used the normals to confirm previous authors' results, including that the layering systems on the comet's two lobes are geometrically independent from each other. My results rule out the proposal that 67P's lobes represent collisional fragments of a much larger, layered body. Second, I developed a Fourier-based image analysis algorithm to detect lineament structures at pixel-precision. I analysed the layering-related features exposed on the Hathor cliff on the comet's Small Lobe. I found my algorithm to be a broadly applicable, powerful tool for automating the detection of layerings in images where conventional edge-detection algorithms are not effective. When correctly configured to the target conditions, I found the algorithm to have a higher signal-to-noise detection sensitivity than a human, while reducing over-interpretation due to bias. In summary, I studied the layerings in the nucleus of comet 67P using several unconventional approaches and constrained their lateral extent, curvature, and to a degree also their thickness. Finally, I nominated two mechanisms that could have formed these layerings in cometary nuclei.Comment: Dissertaton, Gottingen, 11.09.2019. online version with abbreviated Appendix B.4, full version available under ISBN 978-3-947208-20-

Becoming consumers : looking beyond wealth as an explanation for villa variability : perspectives from the East of England

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

ReDreaming Dharawal: A transcultural and multi-disciplined approach to the Aboriginal art and landscapes of southern Sydney

This study addresses post-contact Aboriginal art practices of the southern Sydney region; traditional lands of the Dharawal-speaking peoples. Given that a conventional Western art history has found the pluralistic nature of such work problematic, this study seeks to understand how it might be valued and understood in a wider art-world context. Through extensive field work which included the first survey and analysis of the large body of public art produced in association with Aboriginal people since the Bicentennial, this thesis finds that engagement with non-Aboriginal Australians is an important tactic of Aboriginal people in achieving agency in the modern world; and that, in contrast to assumptions still made about Aboriginal artists working in urban areas, re-establishing and reaffirming relationships with Country remains a core concern. I argue that a multi-disciplined methodology that employs ideas from anthropology, archaeology and human geography offers the best means of comprehending the sensitive, transcultural nature of the art practices and art histories of Dharawal country