A framework for surface metrology on Cultural Heritage objects based on scanning conoscopic holography

L'applicazione della metrologia di superficie e dell'analisi dimensionale allo studio dei beni culturali può rivelare importanti informazioni sull'oggetto e favorire l'integrazione di molteplici tecniche diagnostiche. Tuttavia, l'applicazione di queste discipline ai Beni Culturali richiede particolari requisiti e attenzioni. In questa tesi, presento i risultati dell'implementazione di diversi sistemi di misurazione della superficie basati sul principio della conoscopia olografica. I senori conoscopici sono strumenti capaci di misurare distanze con precisione micrometrica a scale diverse, accoppiati a slitte micrometriche possono essere utilizzati per acquisire scansioni areali dell'oggetto in esame. Per facilitare la loro applicazione alle opere d'arte ho sviluppato un extit{framework} per applicare la metrologia di superficie ai beni culturali. Il framework copre diversi aspetti del processo di analisi ed utilizzo dei dati e comprende la creazione di raccolte di campioni, le strategie per la scansione dell'oggetto, l'archiviazione e l'analisi dei dati ed eventualmente l'incertezza legata alla misura. Il extit{framework} mira a rendere più accessibile l'implementazione della metrologia di superficie e dei sistemi di scansione dell'analisi dimensionale per l'analisi dei beni culturali. I risultati raccolti su una varietà di materiali artistici (metalli, dipinti su tavola, tela, carta, pergamena e dipinti murali) mostrano come questi sistemi possano essere utilizzati per monitorare gli effetti delle procedure di pulitura, la stabilità dimensionale delle opere d'arte ed il loro invecchiamento.The application of surface metrology and dimensional analysis to the study of artworks can reveal important information on the object and aid the integration of multiple techniques. However, the application of these disciplines to Cultural Heritage objects necessitates particular care and requirements. In this dissertation, I present the results of the implementation of different systems, based on Conoscopic Holography range finders, for measuring the surface. Conoscopic holography range finders are viable instruments for measuring distances with micrometer accuracy at different scales, coupled with micrometric stages they can be used for acquiring areal scans of the object under investigation. To ease their application to artworks I built a framework for applying surface metrology to Cultural Heritage objects. The framework covers different aspects of the research workflow comprising the creation of samples collections, the strategies for scanning the object, the storing and the analysis of the data and eventually the uncertainty linked to the measurement. This framework aims to make more accessible the implementation of surface metrology and dimensional analysis scanning systems tailored to the analysis of Cultural Heritage objects. The results collected on a variety of artworks materials (metals, panels painting, canvas, paper, parchment and mural paintings) show how these systems can be used for monitoring the effects of cleaning procedures, the dimensional stability of the artworks and their ageing

BEMDEC: An Adaptive and Robust Methodology for Digital Image Feature Extraction

The intriguing study of feature extraction, and edge detection in particular, has, as a result of the increased use of imagery, drawn even more attention not just from the field of computer science but also from a variety of scientific fields. However, various challenges surrounding the formulation of feature extraction operator, particularly of edges, which is capable of satisfying the necessary properties of low probability of error (i.e., failure of marking true edges), accuracy, and consistent response to a single edge, continue to persist. Moreover, it should be pointed out that most of the work in the area of feature extraction has been focused on improving many of the existing approaches rather than devising or adopting new ones. In the image processing subfield, where the needs constantly change, we must equally change the way we think. In this digital world where the use of images, for variety of purposes, continues to increase, researchers, if they are serious about addressing the aforementioned limitations, must be able to think outside the box and step away from the usual in order to overcome these challenges. In this dissertation, we propose an adaptive and robust, yet simple, digital image features detection methodology using bidimensional empirical mode decomposition (BEMD), a sifting process that decomposes a signal into its two-dimensional (2D) bidimensional intrinsic mode functions (BIMFs). The method is further extended to detect corners and curves, and as such, dubbed as BEMDEC, indicating its ability to detect edges, corners and curves. In addition to the application of BEMD, a unique combination of a flexible envelope estimation algorithm, stopping criteria and boundary adjustment made the realization of this multi-feature detector possible. Further application of two morphological operators of binarization and thinning adds to the quality of the operator

Survival, growth, and radula morphology of postlarval pinto abalone (Haliotis kamtschatkana) when fed six species of benthic diatoms

Haliotis kamtschatkana Jonas (pinto or northern abalone) is the only abalone native to the Pacific Northwest of North America. Haliotis kamtschatkana populations are in decline, and current restoration efforts in Washington State rely on out-planting hatchery-produced juveniles. Although several other abalone species are cultured extensively, little information exists on the cultivation of H. kamtschatkana, and hatchery production of this species has largely been a matter of trial and error. Hatcheries report highest mortalities in the postlarval stage, especially the first 3 to 6 months. Postlarvae feed on films of benthic diatoms, and the purpose of this study was to test 6 benthic diatom species as suitable diatom diets for H. kamtschatkana. Diatom diet suitability might rely on several factors, including morphology of the radula. The radula is a crucial feeding structure for gastropods and may display morphological plasticity, but it has never been characterized in H. kamtschatkana postlarvae. We investigated survival, growth, and radula morphology of H. kamtschatkana postlarvae when fed one of 6 benthic diatom species for 61 days post-settlement. Amphora salina best supported survival, especially in the first 20 days post-settlement (mean of 60% [SD, 22%] at day 20, mean of 47% [SD, 16%] at day 61), and Achnanthes brevipes yielded exceptionally low survival (mean of 12% [SD, 13%] and day 20, mean of 1% [SD, 3%] at day 61). Postlarvae fed Cylindrotheca closterium grew fastest among treatments (linear mixed model shell length = 293*e0.021t, measured 1,110 µm [SD, 244 µm] at day 61), followed by postlarvae fed Amphora salina, Navicula incerta, or Nitzschia laevis (no significant difference between these diets; linear mixed model shell length = 302*e0.018t, measured 894 µm [SD, 132 µm] at day 61). We found no effect of diatom diet on radula morphology, but morphology was similar to that of other abalone species, with similar correlations between morphological characteristics and shell length. We recommend that radula development of other species may be used as a proxy for H. kamtschatkana radula development, in the absence of further investigation. We recommend A. salina as a suitable diet for newly settled H. kamtschatkana postlarvae, and that a combination of A. salina and C. closterium be investigated to support both survival and growth

Analysis of systems hardware flown on LDEF. Results of the systems special investigation group

The Long Duration Exposure Facility (LDEF) was retrieved after spending 69 months in low Earth orbit (LEO). LDEF carried a remarkable variety of mechanical, electrical, thermal, and optical systems, subsystems, and components. The Systems Special Investigation Group (Systems SIG) was formed to investigate the effects of the long duration exposure to LEO on systems related hardware and to coordinate and collate all systems analysis of LDEF hardware. Discussed here is the status of the LDEF Systems SIG investigation through the end of 1991

Assessment of plastics in the National Trust: a case study at Mr Straw's House

The National Trust is a charity that cares for over 300 publically accessible historic buildings and their contents across England, Wales and Northern Ireland. There have been few previous studies on preservation of plastics within National Trust collections, which form a significant part of the more modern collections of objects. This paper describes the design of an assessment system which was successfully trialled at Mr Straws House, a National Trust property in Worksop, UK. This system can now be used for future plastic surveys at other National Trust properties. In addition, the survey gave valuable information about the state of the collection, demonstrating that the plastics that are deteriorating are those that are known to be vulnerable, namely cellulose nitrate/acetate, PVC and rubber. Verifying this knowledge of the most vulnerable plastics enables us to recommend to properties across National Trust that these types should be seen as a priority for correct storage and in-depth recording

Aiding the conservation of two wooden Buddhist sculptures with 3D imaging and spectroscopic techniques

The conservation of Buddhist sculptures that were transferred to Europe at some point during their lifetime raises numerous questions: while these objects historically served a religious, devotional purpose, many of them currently belong to museums or private collections, where they are detached from their original context and often adapted to western taste. A scientific study was carried out to address questions from Museo d'Arte Orientale of Turin curators in terms of whether these artifacts might be forgeries or replicas, and how they may have transformed over time. Several analytical techniques were used for materials identification and to study the production technique, ultimately aiming to discriminate the original materials from those added within later interventions

Pattern classification approaches for breast cancer identification via MRI: state‐of‐the‐art and vision for the future

Mining algorithms for Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCEMRI) of breast tissue are discussed. The algorithms are based on recent advances in multidimensional signal processing and aim to advance current state‐of‐the‐art computer‐aided detection and analysis of breast tumours when these are observed at various states of development. The topics discussed include image feature extraction, information fusion using radiomics, multi‐parametric computer‐aided classification and diagnosis using information fusion of tensorial datasets as well as Clifford algebra based classification approaches and convolutional neural network deep learning methodologies. The discussion also extends to semi‐supervised deep learning and self‐supervised strategies as well as generative adversarial networks and algorithms using generated confrontational learning approaches. In order to address the problem of weakly labelled tumour images, generative adversarial deep learning strategies are considered for the classification of different tumour types. The proposed data fusion approaches provide a novel Artificial Intelligence (AI) based framework for more robust image registration that can potentially advance the early identification of heterogeneous tumour types, even when the associated imaged organs are registered as separate entities embedded in more complex geometric spaces. Finally, the general structure of a high‐dimensional medical imaging analysis platform that is based on multi‐task detection and learning is proposed as a way forward. The proposed algorithm makes use of novel loss functions that form the building blocks for a generated confrontation learning methodology that can be used for tensorial DCE‐MRI. Since some of the approaches discussed are also based on time‐lapse imaging, conclusions on the rate of proliferation of the disease can be made possible. The proposed framework can potentially reduce the costs associated with the interpretation of medical images by providing automated, faster and more consistent diagnosis

Retinal vessel segmentation using textons

Segmenting vessels from retinal images, like segmentation in many other medical image domains, is a challenging task, as there is no unified way that can be adopted to extract the vessels accurately. However, it is the most critical stage in automatic assessment of various forms of diseases (e.g. Glaucoma, Age-related macular degeneration, diabetic retinopathy and cardiovascular diseases etc.). Our research aims to investigate retinal image segmentation approaches based on textons as they provide a compact description of texture that can be learnt from a training set. This thesis presents a brief review of those diseases and also includes their current situations, future trends and techniques used for their automatic diagnosis in routine clinical applications. The importance of retinal vessel segmentation is particularly emphasized in such applications. An extensive review of previous work on retinal vessel segmentation and salient texture analysis methods is presented. Five automatic retinal vessel segmentation methods are proposed in this thesis. The first method focuses on addressing the problem of removing pathological anomalies (Drusen, exudates) for retinal vessel segmentation, which have been identified by other researchers as a problem and a common source of error. The results show that the modified method shows some improvement compared to a previously published method. The second novel supervised segmentation method employs textons. We propose a new filter bank (MR11) that includes bar detectors for vascular feature extraction and other kernels to detect edges and photometric variations in the image. The k-means clustering algorithm is adopted for texton generation based on the vessel and non-vessel elements which are identified by ground truth. The third improved supervised method is developed based on the second one, in which textons are generated by k-means clustering and texton maps representing vessels are derived by back projecting pixel clusters onto hand labelled ground truth. A further step is implemented to ensure that the best combinations of textons are represented in the map and subsequently used to identify vessels in the test set. The experimental results on two benchmark datasets show that our proposed method performs well compared to other published work and the results of human experts. A further test of our system on an independent set of optical fundus images verified its consistent performance. The statistical analysis on experimental results also reveals that it is possible to train unified textons for retinal vessel segmentation. In the fourth method a novel scheme using Gabor filter bank for vessel feature extraction is proposed. The ii method is inspired by the human visual system. Machine learning is used to optimize the Gabor filter parameters. The experimental results demonstrate that our method significantly enhances the true positive rate while maintaining a level of specificity that is comparable with other approaches. Finally, we proposed a new unsupervised texton based retinal vessel segmentation method using derivative of SIFT and multi-scale Gabor filers. The lack of sufficient quantities of hand labelled ground truth and the high level of variability in ground truth labels amongst experts provides the motivation for this approach. The evaluation results reveal that our unsupervised segmentation method is comparable with the best other supervised methods and other best state of the art methods

A hybrid piezoelectric and electrostatic energy harvester for scavenging arterial pulsations

Implantable and wearable biomedical devices suffer from a limited lifespan of on-board batteries which results in a requirement to change the battery or the device itself causing additional physical discomfort. In order to overcome this, various energy harvesters have been developed. The human body possesses several types of energy available for scavenging through appropriately designed energy harvesting devices, while cardiovascular system in particular represents a constant reliable source of mechanical energy from vibration. Most conventional energy harvesters exploit only a single phenomenon, such piezo- or triboelectricity, thus producing reduced power density. As an improvement, hybridisation of energy harvesters intends to negate this drawback by simultaneously scavenging energy by multiple harvesters. In the present work, the reverse electrowetting on dielectric (REWOD) phenomenon is combined with the piezoelectric effect in a proof-of-concept hybrid harvester for scavenging biomechanical energy from arterial or other type pulsations. A mathematical model of the harvester was developed, and a computational investigation using CFD, and fluid-structure interaction simulations were carried out using the COMSOL Multiphysics software. The effect of the materials of piezoelectric film and geometrical features of the harvester on parameters such as the displacement, the frequency of pulsations and the energy produced were studied. An experimental setup that could imitate the displacements caused from arterial pulsations was designed and the produced electrical energy characteristics were analysed. A comparison between experimental and computational data was carried out and demonstrated a good agreement. Dependencies between geometrical parameters and electrical output were obtained, recommendation on piezoelectric materials and design solutions were provided