Automated detection of smuggled high-risk security threats using Deep Learning

The security infrastructure is ill-equipped to detect and deter the smuggling of non-explosive devices that enable terror attacks such as those recently perpetrated in western Europe. The detection of so-called "Small Metallic Threats" (SMTs) in cargo containers currently relies on statistical risk analysis, intelligence reports, and visual inspection of X-ray images by security officers. The latter is very slow and unreliable due to the difficulty of the task: objects potentially spanning less than 50 pixels have to be detected in images containing more than 2 million pixels against very complex and cluttered backgrounds. In this contribution, we demonstrate for the first time the use of Convolutional Neural Networks (CNNs), a type of Deep Learning, to automate the detection of SMTs in fullsize X-ray images of cargo containers. Novel approaches for dataset augmentation allowed to train CNNs from-scratch despite the scarcity of data available. We report fewer than 6% false alarms when detecting 90% SMTs synthetically concealed in stream-of-commerce images, which corresponds to an improvement of over an order of magnitude over conventional approaches such as Bag-of-Words (BoWs). The proposed scheme offers potentially super-human performance for a fraction of the time it would take for a security officers to carry out visual inspection (processing time is approximately 3.5s per container image)

The Atlas Structure of Images

Many operations of vision require image regions to be isolated and inter-related. This is challenging when they are different in detail and extent. Practical methods of Computer Vision approach this through the tools of downsampling, pyramids, cropping and patches. In this paper we develop an ideal geometric structure for this, compatible with the existing scale space model of image measurement. Its elements are apertures which view the image like fuzzy-edged portholes of frosted glass. We establish containment and cause/effect relations between apertures, and show that these link them into cross-scale atlases. Atlases formed of Gaussian apertures are shown to be a continuous version of the image pyramid used in Computer Vision, and allow various types of image description to naturally be expressed within their framework. We show that views through Gaussian apertures are approximately equivalent to the jets of derivative of Gaussian filter responses that form part of standard Scale Space theory. This supports a view of the simple cells of mammalian V1 as implementing a system of local views of the retinal image of varying extent and resolution. As a worked example we develop a keypoint descriptor scheme that outperforms previous schemes that do not make use of learning

Basic image features (BIFs) arising from approximate symmetry type

We consider detection of local image symmetry using linear filters. We prove a simple criterion for determining if a filter is sensitive to a group of symmetries. We show that derivative-of-Gaussian (DtG) filters are excellent at detecting local image symmetry. Building on this, we propose a very simple algorithm that, based on the responses of a bank of six DtG filters, classifies each location of an image into one of seven Basic Image Features (BIFs). This effectively and efficiently realizes Marr's proposal for an image primal sketch. We summarize results on the use of BIFs for texture classification, object category detection, and pixel classification

Basic image features (BIFs) arising from approximate symmetry type

We consider detection of local image symmetry using linear filters. We prove a simple criterion for determining if a filter is sensitive to a group of symmetries. We show that derivative-of-Gaussian (DtG) filters are excellent at detecting local image symmetry. Building on this, we propose a very simple algorithm that, based on the responses of a bank of six DtG filters, classifies each location of an image into one of seven Basic Image Features (BIFs). This effectively and efficiently realizes Marr's proposal for an image primal sketch. We summarize results on the use of BIFs for texture classification, object category detection, and pixel classification

Towards Real-Time Anomaly Detection within X-ray Security Imagery: Self-Supervised Adversarial Training Approach

Automatic threat detection is an increasingly important area in X-ray security imaging since it is critical to aid screening operators to identify concealed threats. Due to the cluttered and occluded nature of X-ray baggage imagery and limited dataset availability, few studies in the literature have systematically evaluated the automated X-ray security screening. This thesis provides an exhaustive evaluation of the use of deep Convolutional Neural Networks (CNN) for the image classification and detection problems posed within the field. The use of transfer learning overcomes the limited availability of the object of interest data examples. A thorough evaluation reveals the superiority of the CNN features over conventional hand-crafted features. Further experimentation also demonstrates the capability of the supervised deep object detection techniques as object localization strategies within cluttered X-ray security imagery. By addressing the limitations of the current X-ray datasets such as annotation and class-imbalance, the thesis subsequently transitions the scope to- wards deep unsupervised techniques for the detection of anomalies based on the training on normal (benign) X-ray samples only. The proposed anomaly detection models within the thesis employ a conditional encoder-decoder generative adversarial network that jointly learns the generation of high-dimensional image space and the inference of latent space — minimizing the distance between these images and the latent vectors during training aids in learning the data distribution for the normal samples. As a result, a larger distance metric from this learned data distribution at inference time is indicative of an outlier from that distribution — an anomaly. Experimentation over several benchmark datasets, from varying domains, shows the model efficacy and superiority over previous state-of-the-art approaches. Based on the current approaches and open problems in deep learning, the thesis finally provides discussion and future directions for X-ray security imagery

Automatic handwriter identification using advanced machine learning

Handwriter identification a challenging problem especially for forensic investigation. This topic has received significant attention from the research community and several handwriter identification systems were developed for various applications including forensic science, document analysis and investigation of the historical documents. This work is part of an investigation to develop new tools and methods for Arabic palaeography, which is is the study of handwritten material, particularly ancient manuscripts with missing writers, dates, and/or places. In particular, the main aim of this research project is to investigate and develop new techniques and algorithms for the classification and analysis of ancient handwritten documents to support palaeographic studies. Three contributions were proposed in this research. The first is concerned with the development of a text line extraction algorithm on colour and greyscale historical manuscripts. The idea uses a modified bilateral filtering approach to adaptively smooth the images while still preserving the edges through a nonlinear combination of neighboring image values. The proposed algorithm aims to compute a median and a separating seam and has been validated to deal with both greyscale and colour historical documents using different datasets. The results obtained suggest that our proposed technique yields attractive results when compared against a few similar algorithms. The second contribution proposes to deploy a combination of Oriented Basic Image features and the concept of graphemes codebook in order to improve the recognition performances. The proposed algorithm is capable to effectively extract the most distinguishing handwriter’s patterns. The idea consists of judiciously combining a multiscale feature extraction with the concept of grapheme to allow for the extraction of several discriminating features such as handwriting curvature, direction, wrinkliness and various edge-based features. The technique was validated for identifying handwriters using both Arabic and English writings captured as scanned images using the IAM dataset for English handwriting and ICFHR 2012 dataset for Arabic handwriting. The results obtained clearly demonstrate the effectiveness of the proposed method when compared against some similar techniques. The third contribution is concerned with an offline handwriter identification approach based on the convolutional neural network technology. At the first stage, the Alex-Net architecture was employed to learn image features (handwritten scripts) and the features obtained from the fully connected layers of the model. Then, a Support vector machine classifier is deployed to classify the writing styles of the various handwriters. In this way, the test scripts can be classified by the CNN training model for further classification. The proposed approach was evaluated based on Arabic Historical datasets; Islamic Heritage Project (IHP) and Qatar National Library (QNL). The obtained results demonstrated that the proposed model achieved superior performances when compared to some similar method

The transfer and persistence of environmental trace indicators, and methods for digital data acquisition from photographs and micrographs: applications for forensic science research

Environmental forms of trace evidence (such as mineral grains, pollen grains, algae, and sediment) can offer valuable insights within forensic casework. An issue facing forensic science as a whole, and these environmental indicators specifically, is a relative dearth of empirical research which would underpin the interpretation of such indicators when attempting forensic reconstruction. This thesis aims to address this lacuna, undertaking experiments to: (1) Explore variables which affect the rates of transfer and persistence, with specific focus upon quartz grains (a terrestrial indicator) and diatom valves (an aquatic indicator) upon footwear materials (a substrate that has been under-represented in past studies); (2) Conduct research into the effects of particle size and morphology upon transfer and persistence; (3) Develop and adapt methodologies to undertake this research. Accordingly, the outputs of this thesis are: (1) The creation of new datasets which could inform the interpretation of these trace indicators within forensic investigations and crime reconstruction scenarios and (2) The development of novel methodologies which could be employed in future research to attempt to accelerate data collection and analysis, without compromising on accuracy. This research is interdisciplinary, combining theory from forensic science, analytical techniques from the environmental sciences, and some elements of image processing and analysis. This research was funded by the Engineering and Physical Sciences Research Council of the United Kingdom through the Security Science Doctoral Training Research Centre (UCL SECReT) based at University College London (EP/G037264/1)

Microfabricated Devices for Adherent Stem Cell Culture

This thesis details the development of a system of microfabricated devices for the adherent culture of stem cells. The multipotency and self renewal of stem cells make them a potentially abundant source of valuable human cells, for both drug screening and regenerative medicine. However, processing stem cells is challenging due to the complexity of whole cell products, the number of process parameters, and the typical use of adherent culture. It is hypothesised that a microfabricated adherent culture system could facilitate process development with minimal use of resources. Furthermore, microfluidic systems offer advantages in spatial and temporal control over the microenvironment that would benefit process development. An existing prototype culture system is critically evaluated by: assessing the design, modelling fluid flow and dissolved oxygen, and successfully co-culturing human embryonic stem cells, on inactivated mouse embryonic fibroblasts, under perfused conditions. The utilisation of reversible seals facilitates the use of standard tissue-culture polystyrene culture surfaces and manual seeding techniques. The evaluation of the prototype system is used to inform improvements to the design, making it easier to use, increasing the robustness, allowing monitoring of whole culture chambers by microscopy, and improving control over mean pericellular dissolved oxygen. Modelling shows the improved culture system also achieves more uniform distribution of both pericellular dissolved oxygen and fluid velocity. The improved culture system shows similar mouse embryonic stem cell seeding behaviour to tissue culture flasks, but, with medium perfused at 300 μl.h 1, mouse embryonic stem cells reach full confluency in less than 48 h, compared with 72 hours for cells maintained statically in flasks. There is also inconclusive data suggesting that the growth rate is limited by pericellular dissolved oxygen and is, therefore, increased and made more uniform by the inclusion of a gas permeable lid system. The reliability, ease of use, comparability with traditional culture systems, and control over process parameters of the improved system should make it a useful tool for stem cell process development