Automatic image annotation system using deep learning method to analyse ambiguous images

Image annotation has gotten a lot of attention recently because of how quickly picture data has expanded. Together with image analysis and interpretation, image annotation, which may semantically describe images, has a variety of uses in allied industries including urban planning engineering. Even without big data and image identification technologies, it is challenging to manually analyze a diverse variety of photos. The improvements to the Automated Image Annotation (AIA) label system have been the subject of several scholarly research. The authors will discuss how to use image databases and the AIA system in this essay. The proposed method extracts image features from photos using an improved VGG-19, and then uses nearby features to automatically forecast picture labels. The proposed study accounts for both correlations between labels and images as well as correlations within images. The number of labels is also estimated using a label quantity prediction (LQP) model, which improves label prediction precision. The suggested method addresses automatic annotation methodologies for pixel-level images of unusual things while incorporating supervisory information via interactive spherical skins. The genuine things that were converted into metadata and identified as being connected to pre-existing categories were categorized by the authors using a deep learning approach called a conventional neural network (CNN) - supervised. Certain object monitoring systems strive for a high item detection rate (true-positive), followed by a low availability rate (false-positive). The authors created a KD-tree based on k-nearest neighbors (KNN) to speed up annotating. In order to take into account for the collected image backdrop. The proposed method transforms the conventional two-class object detection problem into a multi-class classification problem, breaking the separated and identical distribution estimations on machine learning methodologies. It is also simple to use because it only requires pixel information and ignores any other supporting elements from various color schemes. The following factors are taken into consideration while comparing the five different AIA approaches: main idea, significant contribution, computational framework, computing speed, and annotation accuracy. A set of publicly accessible photos that serve as standards for assessing AIA methods is also provided, along with a brief description of the four common assessment signs

Unconventional gas: potential energy market impacts in the European Union

In the interest of effective policymaking, this report seeks to clarify certain controversies and identify key gaps in the evidence-base relating to unconventional gas. The scope of this report is restricted to the economic impact of unconventional gas on energy markets. As such, it principally addresses such issues as the energy mix, energy prices, supplies, consumption, and trade flows. Whilst this study touches on coal bed methane and tight gas, its predominant focus is on shale gas, which the evidence at this time suggests will be the form of unconventional gas with the most growth potential in the short- to medium-term. This report considers the prospects for the indigenous production of shale gas within the EU-27 Member States. It evaluates the available evidence on resource size, extractive technology, resource access and market access. This report also considers the implications for the EU of large-scale unconventional gas production in other parts of the world. This acknowledges the fact that many changes in the dynamics of energy supply can only be understood in the broader global context. It also acknowledges that the EU is a major importer of energy, and that it is therefore heavily affected by developments in global energy markets that are largely out of its control.JRC.F.3-Energy securit

AiREAS: Sustainocracy for a Healthy City

This book describes the coming about and first results of the AiREAS "healthy city" cooperative in the city of Eindhoven and Province of North Brabant in the Netherlands. AiREAS is an initiative focused on the multidisciplinary co-creation of healthy cities using the core human value of human health and air quality as guiding principle for profound regional innovation. The unique group process that followed uses the complexity of the city of Eindhoven as living lab. It is an anthropology based initiative that invites directly to the same table of core innovative responsibility the local government, innovative business partners, scientific insights and research, and civilian participation. The first phase is described here in which the consortium decided to want to make the invisible of air pollution and human exposure visible for the integral innovative participation of all city's core pillars (policy, education, infrastructure, culture and entrepreneurship). The experience is unique in the world and proceeding now with more phases in Eindhoven and the role out of the same working format in other cities. This Brief is made available to inspire the world to address together the most complex issues of our current era: pollution, climate and core human values

Efficient image duplicate detection based on image analysis

This thesis is about the detection of duplicated images. More precisely, the developed system is able to discriminate possibly modified copies of original images from other unrelated images. The proposed method is referred to as content-based since it relies only on content analysis techniques rather than using image tagging as done in watermarking. The proposed content-based duplicate detection system classifies a test image by associating it with a label that corresponds to one of the original known images. The classification is performed in four steps. In the first step, the test image is described by using global statistics about its content. In the second step, the most likely original images are efficiently selected using a spatial indexing technique called R-Tree. The third step consists in using binary detectors to estimate the probability that the test image is a duplicate of the original images selected in the second step. Indeed, each original image known to the system is associated with an adapted binary detector, based on a support vector classifier, that estimates the probability that a test image is one of its duplicate. Finally, the fourth and last step consists in choosing the most probable original by picking that with the highest estimated probability. Comparative experiments have shown that the proposed content-based image duplicate detector greatly outperforms detectors using the same image description but based on a simpler distance functions rather than using a classification algorithm. Additional experiments are carried out so as to compare the proposed system with existing state of the art methods. Accordingly, it also outperforms the perceptual distance function method, which uses similar statistics to describe the image. While the proposed method is slightly outperformed by the key points method, it is five to ten times less complex in terms of computational requirements. Finally, note that the nature of this thesis is essentially exploratory since it is one of the first attempts to apply machine learning techniques to the relatively recent field of content-based image duplicate detection

Adaptable service-system design: an analysis of Shariah finance in Pakistan

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.An adaptable service system adjusts to the operational-level environments of organisations to enable heterogeneous services. This adaptation is important for sustainability and contextual-value (benefit) creation in a service system. Academics, such as those related to the current service-ecosystem concept, acknowledge the significance of this adaptation. However, little is known about a comprehensive adaptation process and how that integrates within a design for a service system. Also, practitioners are inclined towards this development, as the financial regulator in Pakistan has established an “evolutionary framework”. This framework encourages financial institutions to design Shariah finance services (SFS) which respond and evolve to the emergent market environments. The existing SFS models take benefit from Islamic jurisprudence and economics literatures to provide designs for transactions of financial and physical assets. However, the SFS models de-emphasis the intangible service-elements, where the adaptation is more likely to occur. Currently there is a great need for models that could explain the detailed adaptation process and its placement in an SFS design. The aim of this research is to develop, evaluate and theorise a model for conceptualising a holistic adaptable service-system design. The research aim is achieved through the proposal of a novel deferred service-system design (DSD) model. The DSD conceptualises a service-system design that adapts to the operational-level environments of SFS organisations in Pakistan. The DSD has seven constructs: (i) the service creators apply centrally-planned designs to create a service ii) they adapt these designs to meet the requirements of emergent contexts (iii) the service personnel, customers and aiding parties co-create a service by integrating their (iv) roles and actions, (v) resources and usufructs, (vi) rules and control to generate (vii) value. DSD is based on service-system design (SSD) literature, SFS literature and theory of deferred action (TODA) a theory of system and organisation design. A multiple case study strategy is employed to evaluate, extend and theorise the DSD developed in phase I. Qualitative data are collected in four SFS organisations: Islamic commercial bank, Islamic life Takaful, Islamic mutual fund, and Islamic leasing organisation. Thirty-two in-depth narrative interviews of SFS personnel are conducted and analysed using a narrative discourse analysis method. The findings are triangulated by adding focus-group discussions, visualisations and service offering documents. The empirical findings are synthesised with the extant literature to develop a novel and comprehensive DSD in phase II. The findings show that the service co-creators apply a centrally-developed planned design typology (PDT). PDT includes different blends of SFS models (e.g., partnerships, sales, leases, agency and endowment), expected varieties (list, range and negative) and addable-deductible modules. The service co-creators and their inclusive systems (e.g., families, societies, markets, regulators and other government agencies) affect the planned service-system design to adapt or migrate. The service co-creators follow a novel six-step deferred adaptation process (DAP): emergence locale, information diffusion, knowledge diffusion, indexation, specifics evaluation and adaptation/migration. The empirical findings advance our understanding of a service-system design by showing how a planned design enables adaptation through PDT. More importantly, how the service co-creators follow a systematic process, DAP, to attain the desired adaptation or migrate off the scene. The findings also broaden the conceptualisation of SFS by showing how it is co-created by the financial institutions, customers and aiding parties. This is due to the SFS being perceived as a product of financial institution alone. This research also makes a contribution to service visualisation method by extending and using the service blueprint as an additional data-collection and analysis tool. This study provided fourteen implications for the practitioners.Government in Pakistan, the Higher Education Commission of Pakistan and the Institute of Management Sciences, Peshawar Pakistan

Efficient image duplicate detection based on image analysis

This thesis is about the detection of duplicated images. More precisely, the developed system is able to discriminate possibly modified copies of original images from other unrelated images. The proposed method is referred to as content-based since it relies only on content analysis techniques rather than using image tagging as done in watermarking. The proposed content-based duplicate detection system classifies a test image by associating it with a label that corresponds to one of the original known images. The classification is performed in four steps. In the first step, the test image is described by using global statistics about its content. In the second step, the most likely original images are efficiently selected using a spatial indexing technique called R-Tree. The third step consists in using binary detectors to estimate the probability that the test image is a duplicate of the original images selected in the second step. Indeed, each original image known to the system is associated with an adapted binary detector, based on a support vector classifier, that estimates the probability that a test image is one of its duplicate. Finally, the fourth and last step consists in choosing the most probable original by picking that with the highest estimated probability. Comparative experiments have shown that the proposed content-based image duplicate detector greatly outperforms detectors using the same image description but based on a simpler distance functions rather than using a classification algorithm. Additional experiments are carried out so as to compare the proposed system with existing state of the art methods. Accordingly, it also outperforms the perceptual distance function method, which uses similar statistics to describe the image. While the proposed method is slightly outperformed by the key points method, it is five to ten times less complex in terms of computational requirements. Finally, note that the nature of this thesis is essentially exploratory since it is one of the first attempts to apply machine learning techniques to the relatively recent field of content-based image duplicate detection

Modèle de dégradation d’images de documents anciens pour la génération de données semi-synthétiques

In the last two decades, the increase in document image digitization projects results in scientific effervescence for conceiving document image processing and analysis algorithms (handwritten recognition, structure document analysis, spotting and indexing / retrieval graphical elements, etc.). A number of successful algorithms are based on learning (supervised, semi-supervised or unsupervised). In order to train such algorithms and to compare their performances, the scientific community on document image analysis needs many publicly available annotated document image databases. Their contents must be exhaustive enough to be representative of the possible variations in the documents to process / analyze. To create real document image databases, one needs an automatic or a manual annotation process. The performance of an automatic annotation process is proportional to the quality and completeness of these databases, and therefore annotation remains largely manual. Regarding the manual process, it is complicated, subjective, and tedious. To overcome such difficulties, several crowd-sourcing initiatives have been proposed, and some of them being modelled as a game to be more attractive. Such processes reduce significantly the price andsubjectivity of annotation, but difficulties still exist. For example, transcription and textline alignment have to be carried out manually. Since the 1990s, alternative document image generation approaches have been proposed including in generating semi-synthetic document images mimicking real ones. Semi-synthetic document image generation allows creating rapidly and cheaply benchmarking databases for evaluating the performances and trainingdocument processing and analysis algorithms. In the context of the project DIGIDOC (Document Image diGitisation with Interactive DescriptiOn Capability) funded by ANR (Agence Nationale de la Recherche), we focus on semi-synthetic document image generation adapted to ancient documents. First, we investigate new degradation models or adapt existing degradation models to ancient documents such as bleed-through model, distortion model, character degradation model, etc. Second, we apply such degradation models to generate semi-synthetic document image databases for performance evaluation (e.g the competition ICDAR2013, GREC2013) or for performance improvement (by re-training a handwritten recognition system, a segmentation system, and a binarisation system). This research work raises many collaboration opportunities with other researchers to share our experimental results with our scientific community. This collaborative work also helps us to validate our degradation models and to prove the efficiency of semi-synthetic document images for performance evaluation and re-training.Le nombre important de campagnes de numérisation mises en place ces deux dernières décennies a entraîné une effervescence scientifique ayant mené à la création de nombreuses méthodes pour traiter et/ou analyser ces images de documents (reconnaissance d’écriture, analyse de la structure de documents, détection/indexation et recherche d’éléments graphiques, etc.). Un bon nombre de ces approches est basé sur un apprentissage (supervisé, semi supervisé ou non supervisé). Afin de pouvoir entraîner les algorithmes correspondants et en comparer les performances, la communauté scientifique a un fort besoin de bases publiques d’images de documents avec la vérité-terrain correspondante, et suffisamment exhaustive pour contenir des exemples représentatifs du contenu des documents à traiter ou analyser. La constitution de bases d’images de documents réels nécessite d’annoter les données (constituer la vérité terrain). Les performances des approches récentes d’annotation automatique étant très liées à la qualité et à l’exhaustivité des données d’apprentissage, ce processus d’annotation reste très largement manuel. Ce processus peut s’avérer complexe, subjectif et fastidieux. Afin de tenter de pallier à ces difficultés, plusieurs initiatives de crowdsourcing ont vu le jour ces dernières années, certaines sous la forme de jeux pour les rendre plus attractives. Si ce type d’initiatives permet effectivement de réduire le coût et la subjectivité des annotations, reste un certain nombre de difficultés techniques difficiles à résoudre de manière complètement automatique, par exemple l’alignement de la transcription et des lignes de texte automatiquement extraites des images. Une alternative à la création systématique de bases d’images de documents étiquetées manuellement a été imaginée dès le début des années 90. Cette alternative consiste à générer des images semi-synthétiques imitant les images réelles. La génération d’images de documents semi-synthétiques permet de constituer rapidement un volume de données important et varié, répondant ainsi aux besoins de la communauté pour l’apprentissage et l’évaluation de performances de leurs algorithmes. Dans la cadre du projet DIGIDOC (Document Image diGitisation with Interactive DescriptiOn Capability) financé par l’ANR (Agence Nationale de la Recherche), nous avons mené des travaux de recherche relatifs à la génération d’images de documents anciens semi-synthétiques. Le premier apport majeur de nos travaux réside dans la création de plusieurs modèles de dégradation permettant de reproduire de manière synthétique des déformations couramment rencontrées dans les images de documents anciens (dégradation de l’encre, déformation du papier, apparition de la transparence, etc.). Le second apport majeur de ces travaux de recherche est la mise en place de plusieurs bases d’images semi-synthétiques utilisées dans des campagnes de test (compétition ICDAR2013, GREC2013) ou pour améliorer par ré-apprentissage les résultats de méthodes de reconnaissance de caractères, de segmentation ou de binarisation. Ces travaux ont abouti sur plusieurs collaborations nationales et internationales, qui se sont soldées en particulier par plusieurs publications communes. Notre but est de valider de manière la plus objective possible, et en collaboration avec la communauté scientifique concernée, l’intérêt des images de documents anciens semi-synthétiques générées pour l’évaluation de performances et le ré-apprentissage