Colour texture classification from colour filter array images using various colour spaces

International audienceThis paper focuses on the classification of colour textures acquired by single-sensor colour cameras. In such cameras, the Colour Filter Array (CFA) makes each photosensor sensitive to only one colour component, and CFA images must be demosaiced to estimate the final colour images. We show that demosaicing is detrimental to the textural information because it affects colour texture descriptors such as Chromatic Co-occurrence Matrices (CCMs). However, it remains desirable to take advantage of the chromatic information for colour texture classification. This information is incompletely defined in CFA images, in which each pixel is associated to one single colour component. It is hence a challenge to extract standard colour texture descriptors from CFA images without demosaicing. We propose to form a pair of quarter-size colour images directly from CFA images without any estimation, then to compute the CCMs of these quarter-size images. This allows us to compare textures by means of their CCM-based similarity in texture classification or retrieval schemes, with still the ability to use different colour spaces. Experimental results achieved on benchmark colour texture databases show the effectiveness of the proposed approach for texture classification, and a complexity study highlights its computational efficiency

Peeking into the other half of the glass : handling polarization in recommender systems.

This dissertation is about filtering and discovering information online while using recommender systems. In the first part of our research, we study the phenomenon of polarization and its impact on filtering and discovering information. Polarization is a social phenomenon, with serious consequences, in real-life, particularly on social media. Thus it is important to understand how machine learning algorithms, especially recommender systems, behave in polarized environments. We study polarization within the context of the users\u27 interactions with a space of items and how this affects recommender systems. We first formalize the concept of polarization based on item ratings and then relate it to the item reviews, when available. We then propose a domain independent data science pipeline to automatically detect polarization using the ratings rather than the properties, typically used to detect polarization, such as item\u27s content or social network topology. We perform an extensive comparison of polarization measures on several benchmark data sets and show that our polarization detection framework can detect different degrees of polarization and outperforms existing measures in capturing an intuitive notion of polarization. We also investigate and uncover certain peculiar patterns that are characteristic of environments where polarization emerges: A machine learning algorithm finds it easier to learn discriminating models in polarized environments: The models will quickly learn to keep each user in the safety of their preferred viewpoint, essentially, giving rise to filter bubbles and making them easier to learn. After quantifying the extent of polarization in current recommender system benchmark data, we propose new counter-polarization approaches for existing collaborative filtering recommender systems, focusing particularly on the state of the art models based on Matrix Factorization. Our work represents an essential step toward the new research area concerned with quantifying, detecting and counteracting polarization in human-generated data and machine learning algorithms.We also make a theoretical analysis of how polarization affects learning latent factor models, and how counter-polarization affects these models. In the second part of our dissertation, we investigate the problem of discovering related information by recommendation of tags on social media micro-blogging platforms. Real-time micro-blogging services such as Twitter have recently witnessed exponential growth, with millions of active web users who generate billions of micro-posts to share information, opinions and personal viewpoints, daily. However, these posts are inherently noisy and unstructured because they could be in any format, hence making them difficult to organize for the purpose of retrieval of relevant information. One way to solve this problem is using hashtags, which are quickly becoming the standard approach for annotation of various information on social media, such that varied posts about the same or related topic are annotated with the same hashtag. However hashtags are not used in a consistent manner and most importantly, are completely optional to use. This makes them unreliable as the sole mechanism for searching for relevant information. We investigate mechanisms for consolidating the hashtag space using recommender systems. Our methods are general enough that they can be used for hashtag annotation in various social media services such as twitter, as well as for general item recommendations on systems that rely on implicit user interest data such as e-learning and news sites, or explicit user ratings, such as e-commerce and online entertainment sites. To conclude, we propose a methodology to extract stories based on two types of hashtag co-occurrence graphs. Our research in hashtag recommendation was able to exploit the textual content that is available as part of user messages or posts, and thus resulted in hybrid recommendation strategies. Using content within this context can bridge polarization boundaries. However, when content is not available, is missing, or is unreliable, as in the case of platforms that are rich in multimedia and multilingual posts, the content option becomes less powerful and pure collaborative filtering regains its important role, along with the challenges of polarization

REFERENCIAL SEMÂNTICO NO SUPORTE DA IDENTIFICAÇÃO BOTÂNICA DE ESPÉCIES AMAZÔNICAS

A identificação botânica de espécies vegetais nativas da Amazônia é parte integrante do inventário florestal, imprescindível para o plano de manejo florestal e essencial para que a comunidade científica conheça mais e melhor a floresta Amazônica. No entanto, o processo usual de identificação botânica normalmente usa apenas o conhecimento empírico de nativos conhecedores da floresta (mateiros), os quais adotam nomes vernaculares (populares) na determinação das espécies, que por sua vez, apresentam divergêcias dos nomes científicos catalogados por taxonomistas. Tendo esta problemática como cenário de pesquisa, este trabalho propõe um modelo conceitual para suportar um referencial semântico que apoie o processo de identificação de espécies botânicas da Amazônia, com intuito de minimizar as divergências de conhecimento entre taxonomistas e mateiros, e consequentemente aumentar a acurácia do método de identificação. Para tal, são utilizados recursos semânticos (e.g. ontologia e vetores semânticos) na formalização do conhecimento capturado. Dois cenários de aplicação são usados para avaliar este trabalho, nomeadamente: (i) o cenário Inventário Florestal que utiliza como instrumento avaliativo o sistema especialista para identificação botânica por características; (ii) o cenário Imagem Madeira que utiliza como instrumento avaliativo o sistema especialista para classificação de imagem de madeira. Como parte dos resultados, estes cenários utilizam o reconhecimento de padrão no apoio à tomada de decisão usando ferramentas computacionais no auxílio ao processo de identificação de espécies florestais comercializadas na Amazônia, com taxas de acertos de 65% de reconhecimento em imagens de madeira. Por conseguinte conclui-se que o referencial semântico proposto neste trabalho contribui sobremaneira no âmbito ambiental, no que tange à produção de conhecimento sobre a Amazôni

Multi-hazard resilience assessment of river-crossing bridges

Title from PDF of title page viewed December 7, 2020Dissertation advisors: ZhiQiang Chen and Majid Bani-YaghoubVitaIncludes bibliographical references (pages 216-233)Thesis (Ph.D.)--School of Computing and Engineering and Department of Mathematics and Statistics. University of Missouri--Kansas City, 2020Bridge structures are required to possess high reliability and robustness against the concurrent effect of extreme loads and environmental attacks. To achieve such interrelated goals, it is necessary to assess the system performance and resilience subjected to multi-hazard impacts and the beneficial effects of any retrofitting or hazard-countermeasure in a lifecycle context. The damaged bridge needs to be restored rapidly over its service life due to the significant economic losses and disruption to transportation networks. For river-crossing bridges, one of the essential hazard mitigation strategies is scour countermeasures. However, a quantitative understanding of the effects of SCs on bridge system resilience is not found. This dissertation presents a critical synthesis of the existing literature that provides relevant knowledge and a profound understanding of probabilistic multi-hazard assessment for bridge structures. Then, a finite element-based probabilistic framework is designed to assess the lifecycle resilience of reinforced concrete river-crossing bridges under seismic, flood-induced scour, and chloride-induced corrosion impacts, including the consideration of a typical scour countermeasure at variable service times. Based on the general performance-based approach, two probabilistic models are formulated, termed the mean-scour fragility analysis (MS-FA) model and the total-scour demand hazard analysis (TS-DHA) model, which produce straightforward functional curves and can be readily used to evaluate the seismic-scour multi-hazard effects. An integrated damage index is defined based on both local and system-level ductility demands to develop a demand hazard model and to estimate the damage-based residual functionality and recovery duration to quantify the lifecycle bridge resilience. Notably, the exceeding probability approach is designed to reveal how progressive and sudden hazards interact and result in resilience degradation and how scour countermeasures contribute to resilience enhancement. The outcomes of the numerical experiment reveal the positive and distinct effects of implementing SCs at different lifecycle intervals. More importantly, resilience time-series demonstrate arbitrary multi-modes and nonparametric patterns. Accordingly, a robust statistical distance-based approach is presented to determine the sequential evolution of time-varying multi-hazard resilience. The proposed framework would assist stakeholders and decision-makers in resilience patterns recognition, assessing the effectiveness of hazard mitigation strategies, and taking short- and long-term proactive intervention actions by specifying resilience thresholds.Introduction -- Probabilistic multi-hazard performance assessment and damage effects on bridges -- Lifecycle resilience quantification of bridges under multiple hazards -- Effect of scour countermeasure on resilience of river -crossing bridges -- Time-varying resilience quantification using nonparametric distance -- Conclusions and future work -- Appendice

Object tracking in augmented reality remote access laboratories without fiducial markers

Remote Access Laboratories provide students with access to learning resources without the need to be in-situ (with the assets). The technology endows users with access to physical experiments anywhere and anytime, while also minimising or distributing the cost of operation for expensive laboratory equipment. Augmented Reality is a technology which provides interactive sensory feedback to users. The user experiences reality through a computer-based user interface with additional computer-generated information in the form applicable to the targeted senses. Recent advances in high definition video capture devices, video screens and mobile computers have driven resurgence in mainstream Augmented Reality technologies. Lower cost and greater processing power of microprocessors and memory place the resources in the hands of developers and users alike, allowing education institutes to invest in technologies that enhance the delivery of course content. This increase in pedagogical resources has already allowed the phenomenon of education at a distance to reach students from a wide range of demographics, improving access and outcomes in multiple disciplines. Incorporating Augmented Reality into Remote Access Laboratories resources has the benefit of improving overall user immersion into the remote experiment, thus improving student engagement and understanding of the delivered material. Visual implementations of Augmented Reality rely on providing the user with seamless integration of the current environment (through mobile device, desktop PC, or heads up display) with computer generated artificial visual artefacts. Virtual objects must appear in context to the current environment, and respond in a realistic period, or else the user suffers from a disjointed and confusing blend of real and virtual information. Understanding and interacting with the visual scene is controlled through Computer Vision algorithms, and are crucial in ensuring that the AR systems co-operate with the data discovered through the systems. While Augmented Reality has begun to expand in the educational environment, currently, there is still very little overlap of Augmented Reality technologies with Remote Access Laboratories. This research has investigated Computer Vision models that support Augmented Reality technologies such that live video streams from Remote Laboratories are enhanced by synthetic overlays pertinent to the experiments. Orientation of synthetic visual overlays requires knowledge of key reference points, often performed by fiducial markers. Removing the equipment’s need for fiducial markers and a priori knowledge simplifies and accelerates the uptake and expansion of the technology. These works uncover hybrid Computer Vision models which require no prior knowledge of the laboratory environment, including no fiducial markers or tags to track important objects and references. Developed models derive all relevant data from the live video stream and require no previous knowledge regarding the configuration of the physical scene. The new image analysis paradigms, (Two-Dimensional Colour Histograms and Neighbourhood Gradient Signature) improve the current state of markerless tracking through the unique attributes discovered within the sequential video frames. Novel methods are also established, with which to assess and measure the performance of Computer Vision models. Objective ground truth images minimise the level of subjective interference in measuring the efficacy of CV edge and corner detectors. Additionally, locating an effective method to contrast detected attributes associated with an image or object, has provided a means to measure the likelihood of an image match between video frames. In combination with existing material and new contributions, this research demonstrates effective object detection and tracking for Augmented Reality systems within a Remote Access Laboratory environment, with no requirement for fiducial markers, or prior knowledge of the environment. The models that have been proposed in the work can be generalised to be used in any cyber-physical environment that facilitates peripherals such as cameras and other sensors