Automatic X-ray Image Segmentation and Clustering for Threat Detection

Firearms currently pose a known risk at the borders. The enormous number of X-ray images from parcels, luggage and freight coming into each country via rail, aviation and maritime presents a continual challenge to screening officers. To further improve UK capability and aid officers in their search for firearms we suggest an automated object segmentation and clustering architecture to focus officers’ attentions to high-risk threat objects. Our proposal utilizes dual-view single/ dual-energy 2D X-ray imagery and is a blend of radiology, image processing and computer vision concepts. It consists of a triple-layered processing scheme that supports segmenting the luggage contents based on the effective atomic number of each object, which is then followed by a dual-layered clustering procedure. The latter comprises of mild and a hard clustering phase. The former is based on a number of morphological operations obtained from the image-processing domain and aims at disjoining mild-connected objects and to filter noise. The hard clustering phase exploits local feature matching techniques obtained from the computer vision domain, aiming at sub-clustering the clusters obtained from the mild clustering stage. Evaluation on highly challenging single and dual-energy X-ray imagery reveals the architecture’s promising performance

Singular geodesic coordinates for representing diffeomorphic maps in computational anatomy, with application to the morphometry of early Alzheimer's disease in the medial temporal lobe

In this work we develop novel algorithms for building one to one correspondences between anatomical forms by providing a sparse representation of dense registration information. These sparse parameterizations of complex high dimensional data allow robustness in the face of noise and anomalies, and a platform for inference that is effective in the face of multiple comparisons. We review background in the theory of generating smooth, invertible transformations (the diffeomorphism group), and build our parameterization as a function supported on surfaces bounding anatomical structures of interest. We show how dimensionality can be reduced even further and still provide a rich family of mappings using principal component analysis or Laplace Beltrami eigenfunctions supported on the surface. We develop algorithms for surface matching and image matching within this model, and demonstrate the desired robustness by working with published large neuroimaging datasets that include many low quality examples. Finally we turn to addressing challenges associated with some specific data types: images with multiple labels, and longitudinal data. We use the mapping tools developed to draw conclusions about the progression of early Alzheimer's disease in the medial temporal lobe

Proceedings of the VIIth GSCP International Conference

The 7th International Conference of the Gruppo di Studi sulla Comunicazione Parlata, dedicated to the memory of Claire Blanche-Benveniste, chose as its main theme Speech and Corpora. The wide international origin of the 235 authors from 21 countries and 95 institutions led to papers on many different languages. The 89 papers of this volume reflect the themes of the conference: spoken corpora compilation and annotation, with the technological connected fields; the relation between prosody and pragmatics; speech pathologies; and different papers on phonetics, speech and linguistic analysis, pragmatics and sociolinguistics. Many papers are also dedicated to speech and second language studies. The online publication with FUP allows direct access to sound and video linked to papers (when downloaded)

AVATAR - Machine Learning Pipeline Evaluation Using Surrogate Model

© 2020, The Author(s). The evaluation of machine learning (ML) pipelines is essential during automatic ML pipeline composition and optimisation. The previous methods such as Bayesian-based and genetic-based optimisation, which are implemented in Auto-Weka, Auto-sklearn and TPOT, evaluate pipelines by executing them. Therefore, the pipeline composition and optimisation of these methods requires a tremendous amount of time that prevents them from exploring complex pipelines to find better predictive models. To further explore this research challenge, we have conducted experiments showing that many of the generated pipelines are invalid, and it is unnecessary to execute them to find out whether they are good pipelines. To address this issue, we propose a novel method to evaluate the validity of ML pipelines using a surrogate model (AVATAR). The AVATAR enables to accelerate automatic ML pipeline composition and optimisation by quickly ignoring invalid pipelines. Our experiments show that the AVATAR is more efficient in evaluating complex pipelines in comparison with the traditional evaluation approaches requiring their execution

Unraveling the Thousand Word Picture: An Introduction to Super-Resolution Data Analysis

Super-resolution microscopy provides direct insight into fundamental biological processes occurring at length scales smaller than light’s diffraction limit. The analysis of data at such scales has brought statistical and machine learning methods into the mainstream. Here we provide a survey of data analysis methods starting from an overview of basic statistical techniques underlying the analysis of super-resolution and, more broadly, imaging data. We subsequently break down the analysis of super-resolution data into four problems: the localization problem, the counting problem, the linking problem, and what we’ve termed the interpretation problem

Mathematical linguistics

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Automatic analysis of malaria infected red blood cell digitized microscope images

Malaria is one of the three most serious diseases worldwide, affecting millions each year, mainly in the tropics where the most serious illnesses are caused by Plasmodium falciparum. This thesis is concerned with the automatic analysis of images of microscope slides of Giemsa stained thin-films of such malaria infected blood so as to segment red-blood cells (RBCs) from the background plasma, to accurately and reliably count the cells, identify those that were infected with a parasite, and thus to determine the degree of infection or parasitemia. Unsupervised techniques were used throughout owing to the difficulty of obtaining large quantities of training data annotated by experts, in particular for total RBC counts. The first two aims were met by optimisation of Fisher discriminants. For RBC segmentation, a well-known iterative thresholding method due originally to Otsu (1979) was used for scalar features such as the image intensity and a novel extension of the algorithm developed for multi-dimensional, colour data. Performance of the algorithms was evaluated and compared via ROC analysis and their convergence properties studied. Ways of characterising the variability of the image data and, if necessary of mitigating it, were discussed in theory. The size distribution of the objects segmented in this way indicated that optimisation of a Fisher discriminant could be further used for classifying objects as small artefacts, singlet RBCs, doublets, or triplets etc. of adjoining cells provided optimisation was via a global search. Application of constraints on the relationships between the sizes of singlet and multiplet RBCs led to a number of tests that enabled clusters of cells to be reliably identified and accurate total RBC counts to be made. Development of an application to make such counts could be very useful both in research laboratories and in improving treatment of malaria. Unfortunately, the very small number of pixels belonging to parasite infections mean that it is difficult to segment parasite objects and thus to identify infected RBCs and to determine the parasitemia. Preliminary attempts to do so by similar, unsupervised means using Fischer discriminants, even when applied in a hierarchical manner, though suggestive that it may ultimately be possible to develop such a system remain on the evidence currently available, inconclusive. Appendices give details of material from old texts no longer easily accessible