Measurement limitations in knife-edge tomographic phase retrieval of focused IR laser beams

An experimental setup to measure the three-dimensional phase-intensity distribution of an infrared laser beam in the focal region has been presented. It is based on the knife-edge method to perform a tomographic reconstruction and on a transport of intensity equation-based numerical method to obtain the propagating wavefront. This experimental approach allows us to characterize a focalized laser beam when the use of image or interferometer arrangements is not possible. Thus, we have recovered intensity and phase of an aberrated beam dominated by astigmatism. The phase evolution is fully consistent with that of the beam intensity along the optical axis. Moreover, this method is based on an expansion on both the irradiance and the phase information in a series of Zernike polynomials. We have described guidelines to choose a proper set of these polynomials depending on the experimental conditions and showed that, by abiding these criteria, numerical errors can be reduced.

Advanced Techniques for Image Quality Assessment of Modern X-ray Computed Tomography Systems

X-ray computed tomography (CT) imaging constitutes one of the most widely used diagnostic tools in radiology today with nearly 85 million CT examinations performed in the U.S in 2011. CT imparts a relatively high amount of radiation dose to the patient compared to other x-ray imaging modalities and as a result of this fact, coupled with its popularity, CT is currently the single largest source of medical radiation exposure to the U.S. population. For this reason, there is a critical need to optimize CT examinations such that the dose is minimized while the quality of the CT images is not degraded. This optimization can be difficult to achieve due to the relationship between dose and image quality. All things being held equal, reducing the dose degrades image quality and can impact the diagnostic value of the CT examination.

A recent push from the medical and scientific community towards using lower doses has spawned new dose reduction technologies such as automatic exposure control (i.e., tube current modulation) and iterative reconstruction algorithms. In theory, these technologies could allow for scanning at reduced doses while maintaining the image quality of the exam at an acceptable level. Therefore, there is a scientific need to establish the dose reduction potential of these new technologies in an objective and rigorous manner. Establishing these dose reduction potentials requires precise and clinically relevant metrics of CT image quality, as well as practical and efficient methodologies to measure such metrics on real CT systems. The currently established methodologies for assessing CT image quality are not appropriate to assess modern CT scanners that have implemented those aforementioned dose reduction technologies.

Thus the purpose of this doctoral project was to develop, assess, and implement new phantoms, image quality metrics, analysis techniques, and modeling tools that are appropriate for image quality assessment of modern clinical CT systems. The project developed image quality assessment methods in the context of three distinct paradigms, (a) uniform phantoms, (b) textured phantoms, and (c) clinical images.

The work in this dissertation used the “task-based” definition of image quality. That is, image quality was broadly defined as the effectiveness by which an image can be used for its intended task. Under this definition, any assessment of image quality requires three components: (1) A well defined imaging task (e.g., detection of subtle lesions), (2) an “observer” to perform the task (e.g., a radiologists or a detection algorithm), and (3) a way to measure the observer’s performance in completing the task at hand (e.g., detection sensitivity/specificity).

First, this task-based image quality paradigm was implemented using a novel multi-sized phantom platform (with uniform background) developed specifically to assess modern CT systems (Mercury Phantom, v3.0, Duke University). A comprehensive evaluation was performed on a state-of-the-art CT system (SOMATOM Definition Force, Siemens Healthcare) in terms of noise, resolution, and detectability as a function of patient size, dose, tube energy (i.e., kVp), automatic exposure control, and reconstruction algorithm (i.e., Filtered Back-Projection– FPB vs Advanced Modeled Iterative Reconstruction– ADMIRE). A mathematical observer model (i.e., computer detection algorithm) was implemented and used as the basis of image quality comparisons. It was found that image quality increased with increasing dose and decreasing phantom size. The CT system exhibited nonlinear noise and resolution properties, especially at very low-doses, large phantom sizes, and for low-contrast objects. Objective image quality metrics generally increased with increasing dose and ADMIRE strength, and with decreasing phantom size. The ADMIRE algorithm could offer comparable image quality at reduced doses or improved image quality at the same dose (increase in detectability index by up to 163% depending on iterative strength). The use of automatic exposure control resulted in more consistent image quality with changing phantom size.

Based on those results, the dose reduction potential of ADMIRE was further assessed specifically for the task of detecting small (<=6 mm) low-contrast (<=20 HU) lesions. A new low-contrast detectability phantom (with uniform background) was designed and fabricated using a multi-material 3D printer. The phantom was imaged at multiple dose levels and images were reconstructed with FBP and ADMIRE. Human perception experiments were performed to measure the detection accuracy from FBP and ADMIRE images. It was found that ADMIRE had equivalent performance to FBP at 56% less dose.

Using the same image data as the previous study, a number of different mathematical observer models were implemented to assess which models would result in image quality metrics that best correlated with human detection performance. The models included naïve simple metrics of image quality such as contrast-to-noise ratio (CNR) and more sophisticated observer models such as the non-prewhitening matched filter observer model family and the channelized Hotelling observer model family. It was found that non-prewhitening matched filter observers and the channelized Hotelling observers both correlated strongly with human performance. Conversely, CNR was found to not correlate strongly with human performance, especially when comparing different reconstruction algorithms.

The uniform background phantoms used in the previous studies provided a good first-order approximation of image quality. However, due to their simplicity and due to the complexity of iterative reconstruction algorithms, it is possible that such phantoms are not fully adequate to assess the clinical impact of iterative algorithms because patient images obviously do not have smooth uniform backgrounds. To test this hypothesis, two textured phantoms (classified as gross texture and fine texture) and a uniform phantom of similar size were built and imaged on a SOMATOM Flash scanner (Siemens Healthcare). Images were reconstructed using FBP and a Sinogram Affirmed Iterative Reconstruction (SAFIRE). Using an image subtraction technique, quantum noise was measured in all images of each phantom. It was found that in FBP, the noise was independent of the background (textured vs uniform). However, for SAFIRE, noise increased by up to 44% in the textured phantoms compared to the uniform phantom. As a result, the noise reduction from SAFIRE was found to be up to 66% in the uniform phantom but as low as 29% in the textured phantoms. Based on this result, it clear that further investigation was needed into to understand the impact that background texture has on image quality when iterative reconstruction algorithms are used.

To further investigate this phenomenon with more realistic textures, two anthropomorphic textured phantoms were designed to mimic lung vasculature and fatty soft tissue texture. The phantoms (along with a corresponding uniform phantom) were fabricated with a multi-material 3D printer and imaged on the SOMATOM Flash scanner. Scans were repeated a total of 50 times in order to get ensemble statistics of the noise. A novel method of estimating the noise power spectrum (NPS) from irregularly shaped ROIs was developed. It was found that SAFIRE images had highly locally non-stationary noise patterns with pixels near edges having higher noise than pixels in more uniform regions. Compared to FBP, SAFIRE images had 60% less noise on average in uniform regions for edge pixels, noise was between 20% higher and 40% lower. The noise texture (i.e., NPS) was also highly dependent on the background texture for SAFIRE. Therefore, it was concluded that quantum noise properties in the uniform phantoms are not representative of those in patients for iterative reconstruction algorithms and texture should be considered when assessing image quality of iterative algorithms.

The move beyond just assessing noise properties in textured phantoms towards assessing detectability, a series of new phantoms were designed specifically to measure low-contrast detectability in the presence of background texture. The textures used were optimized to match the texture in the liver regions actual patient CT images using a genetic algorithm. The so called “Clustured Lumpy Background” texture synthesis framework was used to generate the modeled texture. Three textured phantoms and a corresponding uniform phantom were fabricated with a multi-material 3D printer and imaged on the SOMATOM Flash scanner. Images were reconstructed with FBP and SAFIRE and analyzed using a multi-slice channelized Hotelling observer to measure detectability and the dose reduction potential of SAFIRE based on the uniform and textured phantoms. It was found that at the same dose, the improvement in detectability from SAFIRE (compared to FBP) was higher when measured in a uniform phantom compared to textured phantoms.

The final trajectory of this project aimed at developing methods to mathematically model lesions, as a means to help assess image quality directly from patient images. The mathematical modeling framework is first presented. The models describe a lesion’s morphology in terms of size, shape, contrast, and edge profile as an analytical equation. The models can be voxelized and inserted into patient images to create so-called “hybrid” images. These hybrid images can then be used to assess detectability or estimability with the advantage that the ground truth of the lesion morphology and location is known exactly. Based on this framework, a series of liver lesions, lung nodules, and kidney stones were modeled based on images of real lesions. The lesion models were virtually inserted into patient images to create a database of hybrid images to go along with the original database of real lesion images. ROI images from each database were assessed by radiologists in a blinded fashion to determine the realism of the hybrid images. It was found that the radiologists could not readily distinguish between real and virtual lesion images (area under the ROC curve was 0.55). This study provided evidence that the proposed mathematical lesion modeling framework could produce reasonably realistic lesion images.

Based on that result, two studies were conducted which demonstrated the utility of the lesion models. The first study used the modeling framework as a measurement tool to determine how dose and reconstruction algorithm affected the quantitative analysis of liver lesions, lung nodules, and renal stones in terms of their size, shape, attenuation, edge profile, and texture features. The same database of real lesion images used in the previous study was used for this study. That database contained images of the same patient at 2 dose levels (50% and 100%) along with 3 reconstruction algorithms from a GE 750HD CT system (GE Healthcare). The algorithms in question were FBP, Adaptive Statistical Iterative Reconstruction (ASiR), and Model-Based Iterative Reconstruction (MBIR). A total of 23 quantitative features were extracted from the lesions under each condition. It was found that both dose and reconstruction algorithm had a statistically significant effect on the feature measurements. In particular, radiation dose affected five, three, and four of the 23 features (related to lesion size, conspicuity, and pixel-value distribution) for liver lesions, lung nodules, and renal stones, respectively. MBIR significantly affected 9, 11, and 15 of the 23 features (including size, attenuation, and texture features) for liver lesions, lung nodules, and renal stones, respectively. Lesion texture was not significantly affected by radiation dose.

The second study demonstrating the utility of the lesion modeling framework focused on assessing detectability of very low-contrast liver lesions in abdominal imaging. Specifically, detectability was assessed as a function of dose and reconstruction algorithm. As part of a parallel clinical trial, images from 21 patients were collected at 6 dose levels per patient on a SOMATOM Flash scanner. Subtle liver lesion models (contrast = -15 HU) were inserted into the raw projection data from the patient scans. The projections were then reconstructed with FBP and SAFIRE (strength 5). Also, lesion-less images were reconstructed. Noise, contrast, CNR, and detectability index of an observer model (non-prewhitening matched filter) were assessed. It was found that SAFIRE reduced noise by 52%, reduced contrast by 12%, increased CNR by 87%. and increased detectability index by 65% compared to FBP. Further, a 2AFC human perception experiment was performed to assess the dose reduction potential of SAFIRE, which was found to be 22% compared to the standard of care dose.

In conclusion, this dissertation provides to the scientific community a series of new methodologies, phantoms, analysis techniques, and modeling tools that can be used to rigorously assess image quality from modern CT systems. Specifically, methods to properly evaluate iterative reconstruction have been developed and are expected to aid in the safe clinical implementation of dose reduction technologies.

El ritual en los "Textos de las Pirámides": sintaxis, texto y significado

Este trabajo pretende explorar la dimensión ritual en los Textos de las Pirámides, el corpus de literatura religiosa extensa más antiguo de la humanidad. La naturaleza variada de sus componentes textuales ha impedido que los egiptólogos comprendan en profundidad las complejidades de la colección y los contextos originales en los que estos textos (ritos) aparecieron. La aplicación de la teoría del ritual, principalmente la aproximación de la sintaxis ritual, ofrece a los investigadores un marco excelente de análisis e interpretación del corpus, su estructura y función. Sujeto a las reglas de la sintaxis ritual es posible exponer los múltiples niveles de significado en el corpus para la resurrección y salvación del difunto.

D6.3 – Semantic Content Annotation Support

The Semantic Annotation component is a software application that provides support for automated text classification, a process grounded in a cohesion-centered representation of discourse that facilitates topic extraction. The component enables the semantic meta-annotation of text resources, including automated classification, thus facilitating information retrieval within the RAGE ecosystem. It is available in the ReaderBench framework (http://readerbench.com/) which integrates advanced Natural Language Processing (NLP) techniques. The component makes use of Cohesion Network Analysis (CNA) in order to ensure an in-depth representation of discourse, useful for mining keywords and performing automated text categorization. Our component automatically classifies documents into the categories provided by the ACM Computing Classification System (http://dl.acm.org/ccs_flat.cfm), but also into the categories from a high level serious games categorization provisionally developed by RAGE. English and French languages are already covered by the provided web service, whereas the entire framework can be extended in order to support additional languages.

Semantic feature-based visual attention model for pedestrian detection

Objective
Pedestrian detection under video surveillance systems has always been a hot topic in computer vision research. These systems are widely used in train stations, airports, large commercial plazas, and other public places. However, pedestrian detection remains difficult because of complex backgrounds. Given its development in recent years, the visual attention mechanism has attracted increasing attention in object detection and tracking research, and previous studies have achieved substantial progress and breakthroughs. We propose a novel pedestrian detection method based on the semantic features under the visual attention mechanism.
Method
The proposed semantic feature-based visual attention model is a spatial-temporal model that consists of two parts: the static visual attention model and the motion visual attention model. The static visual attention model in the spatial domain is constructed by combining bottom-up with top-down attention guidance. Based on the characteristics of pedestrians, the bottom-up visual attention model of Itti is improved by intensifying the orientation vectors of elementary visual features to make the visual saliency map suitable for pedestrian detection. In terms of pedestrian attributes, skin color is selected as a semantic feature for pedestrian detection. The regional and Gaussian models are adopted to construct the skin color model. Skin feature-based visual attention guidance is then proposed to complete the top-down process. The bottom-up and top-down visual attentions are linearly combined using the proper weights obtained from experiments to construct the static visual attention model in the spatial domain. The spatial-temporal visual attention model is then constructed via the motion features in the temporal domain. Based on the static visual attention model in the spatial domain, the frame difference method is combined with optical flowing to detect motion vectors. Filtering is applied to process the field of motion vectors. The saliency of motion vectors can be evaluated via motion entropy to make the selected motion feature more suitable for the spatial-temporal visual attention model.
Result
Standard datasets and practical videos are selected for the experiments. The experiments are performed on a MATLAB R2012a platform. The experimental results show that our spatial-temporal visual attention model demonstrates favorable robustness under various scenes, including indoor train station surveillance videos and outdoor scenes with swaying leaves. Our proposed model outperforms the visual attention model of Itti, the graph-based visual saliency model, the phase spectrum of quaternion Fourier transform model, and the motion channel model of Liu in terms of pedestrian detection. The proposed model achieves a 93% accuracy rate on the test video.
Conclusion
This paper proposes a novel pedestrian method based on the visual attention mechanism. A spatial-temporal visual attention model that uses low-level and semantic features is proposed to calculate the saliency map. Based on this model, the pedestrian targets can be detected through focus of attention shifts. The experimental results verify the effectiveness of the proposed attention model for detecting pedestrians.

Evaluating Document Retrieval Methods for Resource Selection in Clustered P2P IR

Resource Selection (or Query Routing) is an important step in P2P IR. Though analogous to document retrieval in the sense of choosing a relevant subset of resources, resource selection methods have evolved independently from those for document retrieval. Among the reasons for such divergence is that document retrieval targets scenarios where underlying resources are semantically homogeneous, whereas peers would manage diverse content. We observe that semantic heterogeneity is mitigated in the clustered 2-tier P2P IR architecture resource selection layer by way of usage of clustering, and posit that this necessitates a re-look at the applicability of document retrieval methods for resource selection within such a framework. This paper empirically benchmarks document retrieval models against the state-of-the-art resource selection models for the problem of resource selection in the clustered P2P IR architecture, using classical IR evaluation metrics. Our benchmarking study illustrates that document retrieval models significantly outperform other methods for the task of resource selection in the clustered P2P IR architecture. This indicates that clustered P2P IR framework can exploit advancements in document retrieval methods to deliver corresponding improvements in resource selection, indicating potential convergence of these fields for the clustered P2P IR architecture.

The Role of Luxury Fashion Brands : A qualitative research of how the consumption of luxury fashion brands relate to young consumers' self-concept

The market for luxury brands has outpaced other consumption categories through its growth, and has been found in continuous development. As there is an increasing amount of luxury categories, the consumption of luxury fashion brands account for the largest proportion of luxury profits, and the marketing costs for such brands has shown to surpass those of other fashion categories. Consumer researchers have throughout decades emphasized how individuals participate in consumption behavior to form their self-concept in relation to brands. However, previous research has disregarded the multidimensional perspective regarding the theory of self-concept when examining the consumption of brands. Hence, the current research paper aims to strengthen the existing self-concept theory by exploring the role in which luxury fashion brands have by focusing on how the consumption of such brands relate, and contribute, to the consumer’s self-concept. By applying a qualitative method to investigate such purpose, and involving the existing theory of self-concept, brand image, and brand personality, it appeared that luxury fashion brands has a function to operate as a confidence booster for young consumers’ perception of their self-concept. In terms of the theoretical contribution of this paper, this research further illustrates how the theoretical explanation of brand image and brand personality relates to two different dimensions of the consumer’s self-concept. The consumption of luxury fashion brands has shown a significant role in individuals’ consumption behavior by emphasizing a striving, and motivating, part in the self-concept of young consumers.

Two Women; On the Cinematic Experience in Installation Art

Thesis (Ph.D.)--University of Washington, 2016-07

Exploring the World's Visual History

Thesis (Ph.D.)--University of Washington, 2016-08

Importance du concept "made-in" dans un contexte d’évaluation multi-attributs/multi-produits : effet modérateur des variables de personnalité

Le traité du libre-échange entre les États-Unis et le Canada, l'intégration économique des 12 pays d'Europe et les changements politiques et socio-économiques survenus dans les pays de l'Est sont des exemples d'événements qui ont marqué la fin de la dernière décennie. Ces événements ont pour effet de modifier les règles du jeu de la concurrence internationale en matière de commerce et d'affecter directement les activités des entreprises oeuvrant aussi bien dans les marchés étrangers que domestiques. La firme doit avoir la capacité de connaître davantage le comportement de la clientèle-cible et celui de la concurrence afin de mieux adapter ses stratégies de marketing à cette nouvelle réalité. Les décisions auxquelles les exportateurs et les producteurs locaux ont toujours fait face ont trait à des variables stratégiques, entre autres le prix, le nom de la marque, la promotion, la distribution, le service et le lieu de fabrication ("Made-In"). Cette dernière variable a toujours été d'une grande importance pour les firmes ayant des opérations étrangères; en effet, elle touche deux aspects décisionnels à savoir, le coût d'installation des unités de production dans un pays étranger et la réputation ou l'image de ce dernier chez le consommateur. La présence de produits importés à côté des produits domestiques augmente l’éventail de choix du consommateur. Cependant, sa préférence pour un produit par rapport à un autre dépend de plusieurs éléments, soit des facteurs reliés au produit comme son prix compétitif ou sa qualité supérieure, ou bien des facteurs de personnalité, comme le besoin de prestige et d'appartenance.

Le concept des principes féminin et masculin appliqué à l'orientation

Depuis l'avènement de la révolution industrielle, la société se transforme à un rythme effarant. Pour ne donner que quelques exemples, pensons à la place qu'occupent maintenant les jeunes sur le marché du travail, à la hausse de la scolarisation, mais aussi, du décrochage scolaire, à la montée de la violence, de la pauvreté et du racisme, au marché du travail qui se veut de plus en plus exigeant, au taux de chômage élevé et nous aurons un aperçu des nombreux bouleversements qui ébranlent la société. Cette période de grande transformation est à l'image de celle que peut vivre l'être humain lors de ses étapes de transition, c'est-à-dire qu'elle est souvent difficile à traverser, suscitant le doute, la peur, la résistance au changement. Plusieurs auteurs ont exploré les causes possibles de la stagnation et de l'impuissance à trouver des solutions vraiment efficaces aux nombreux défis qui sont posés. Parmi les réponses proposées, la notion des principes féminin et masculin "refait surface" et apporte un éclairage que l'on pourrait qualifier de philosophique ou de spirituel; c'est cette vision de la vie et de l'être humain que nous allons présenter dans le cadre de cet essai. C'est ainsi qu'après avoir établi un bref constat des malaises collectifs "de l'heure", nous présenterons plus spécifiquement individuels et la notion des principes féminin et masculin en utilisant une perspective historique qui nous fera remonter jusqu'à leur origine. Nous appliquerons ensuite ce concept à l'orientation et nous verrons comment il peut être utilisé à l'intérieur de la pratique carriérologique. Pour ce faire, nous présenterons en premier lieu notre cadre de référence qui s'appuie sur le courant cognitif développemental ainsi que sur la notion de résistance au changement. Nous traiterons ensuite plus particulièrement du diagnostic, du traitement et de la finalité qui sont trois voies possibles à emprunter en regard de ce concept. Enfin, nous discuterons brièvement du rôle du conseiller d'orientation, et ceci, aux niveaux individuel et collectif. Les enjeux de l'orientation étant particulièrement liés à l'expression de soi, nous tenterons de démontrer la pertinence pour l'être humain d'enrichir son répertoire de comportement en reprenant contact avec l'autre en soi: le masculin ou le féminin.

Three-dimensional, in-silico breast phantom for multimodality image simulations

We report on the construction of anatomically realistic three-dimensional in-silico breast phantoms with adjustable sizes, shapes and morphologic features. The concept of multiscale spatial resolution is implemented for generating breast tissue images from multiple modalities. Breast epidermal boundary and subcutaneous fat layer is generated by fitting an ellipsoid and 2nd degree polynomials to reconstructive surgical data and ultrasound imaging data. Intraglandular fat is simulated by randomly distributing and orienting adipose ellipsoids within a fibrous region immediately within the dermal layer. Cooper’s ligaments are simulated as fibrous ellipsoidal shells distributed within the subcutaneous fat layer. Individual ductal lobes are simulated following a random binary tree model which is generated based upon probabilistic branching conditions described by ramification matrices, as originally proposed by Bakic et al [3, 4]. The complete ductal structure of the breast is simulated from multiple lobes that extend from the base of the nipple and branch towards the chest wall. As lobe branching progresses, branches are reduced in height and radius and terminal branches are capped with spherical lobular clusters. Biophysical parameters are mapped onto the complete anatomical model and synthetic multimodal images (Mammography, Ultrasound, CT) are generated for phantoms of different adipose percentages (40%, 50%, 60%, and 70%) and are analytically compared with clinical examples. Results demonstrate that the in-silico breast phantom has applications in imaging performance evaluation and, specifically, great utility for solving image registration issues in multimodality imaging.

Le concept d'expérience chez John Dewey et la problématique de l'éducation morale

L'éducation morale peut-elle répondre au défi et au besoin d'une éthique personnelle et sociale? Comment l'école peut-elle mettre en branle un processus de socialisation chez les jeunes? Permet-elle d'éclairer, d'élargir et d'approfondir le contenu social des enjeux éthiques chez l'enfant quand celui-ci est aux prises constamment avec le devoir du rendement et des notes? La pédagogie de l'enseignement moral telle que vécue dans nos écoles ouvre-telle les portes à la socialisation ou au narcissisme et à l'individualisme? Y a-t-il, en fait, entre l'organisation scolaire et l'organisation sociale, une continuité grâce à laquelle la formation morale à l'école permet au jeune de s'engager dans le processus social tout en développant des connaissances et des aptitudes nécessaires pour comprendre les enjeux éthiques collectifs et proposer des pistes de solutions? Pour favoriser cette continuité, l'organisation scolaire ne devrait-elle pas être à l'image de l'organisation sociale? Ultimement, quel lien existe-t-il entre l'école québécoise et notre société? Ce rapide survol de la problématique de l'éducation morale nous permet de distinguer actuellement trois niveaux d'interrogation: la conception de l'être humain sous-jacente aux programmes, la finalité de l'enseignement dans les écoles du Québec, ainsi que son enjeu social. Soulever ainsi cette problématique nous aide à mieux réfléchir sur la situation et à proposer des pistes de solutions pour faire de l'éducation morale une théorie et une pratique toujours plus conformes aux expériences individuelles et sociales de chez nous. C'est à partir de ce questionnement global que le philosophe et pédagogue américain John Dewey (1859-1952) nous semble très pertinent. Face à la problématique de l'éducation morale au Québec, la référence spécifique à John Dewey nous semble crédible pour plusieurs raisons. […]

A Comparison of Geo-tagging in Mobile Internet Browsing Applications on iOS and Android.

Nowadays there is almost no crime committed without a trace of digital evidence, and since the advanced functionality of mobile devices today can be exploited to assist in crime, the need for mobile forensics is imperative. Many of the mobile applications available today, including internet browsers, will request the user’s permission to access their current location when in use. This geolocation data is subsequently stored and managed by that application's underlying database files. If recovered from a device during a forensic investigation, such GPS evidence and track points could hold major evidentiary value for a case. The aim of this paper is to examine and compare to what extent geolocation data is available from the iOS and Android operating systems. We focus particularly on geolocation data recovered from internet browsing applications, comparing the native Safari and Browser apps with Google Chrome, downloaded on to both platforms. All browsers were used over a period of several days at various locations to generate comparable test data for analysis. Results show considerable differences not only in the storage locations and formats, but also in the amount of geolocation data stored by different browsers and on different operating systems.

Deep convolutional neural networks for semantic video object segmentation

In this thesis, we propose to infer pixel-level labelling in video by utilising only object category information, exploiting the intrinsic structure of video data. Our motivation is the observation that image-level labels are much more easily to be acquired than pixel-level labels, and it is natural to find a link between the image level recognition and pixel level classification in video data, which would transfer learned recognition models from one domain to the other one. To this end, this thesis proposes two domain adaptation approaches to adapt the deep convolutional neural network (CNN) image recognition model trained from labelled image data to the target domain exploiting both semantic evidence learned from CNN, and the intrinsic structures of unlabelled video data. Our proposed approaches explicitly model and compensate for the domain adaptation from the source domain to the target domain which in turn underpins a robust semantic object segmentation method for natural videos. We demonstrate the superior performance of our methods by presenting extensive evaluations on challenging datasets comparing with the state-of-the-art methods.