1,348 research outputs found

    Automatic thresholding from the gradients of region boundaries

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    We present an approach for automatic threshold segmentation of greyscale images. The procedure is inspired by a reinterpretation of the strategy observed in human operators when adjusting thresholds manually and interactively by means of ‘slider’ controls. The approach translates into two methods. The first one is suitable for single or multiple global thresholds to be applied globally to images and consists of searching for a threshold value that generates a phase whose boundary coincides with the largest gradients in the original image. The second method is a variation, implemented to operate on the discrete connected components of the thresholded phase (i.e. the binary regions) independently. Consequently, this becomes an adaptive local threshold procedure, which operates relative to regions, rather than to local image subsets as is the case in most local thresholding methods previously published. Adding constraints for specifying certain classes of expected objects in the images can improve the output of the method over the traditional ‘segmenting first, then classify’ approach.The research reported in this paper was supported by the Engineering and Physical Sciences Research Council (EPSRC), UK through funding under grant EP/M023869/1 ‘Novel contextbased segmentation algorithms for intelligent microscopy’

    EGO: a personalised multimedia management tool

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    The problems of Content-Based Image Retrieval (CBIR) sys- tems can be attributed to the semantic gap between the low-level data representation and the high-level concepts the user associates with images, on the one hand, and the time-varying and often vague nature of the underlying information need, on the other. These problems can be addressed by improving the interaction between the user and the system. In this paper, we sketch the development of CBIR interfaces, and introduce our view on how to solve some of the problems of the studied interfaces. To address the semantic gap and long-term multifaceted information needs, we propose a "retrieval in context" system. EGO is a tool for the management of image collections, supporting the user through personalisation and adaptation. We will describe how it learns from the user's personal organisation, allowing it to recommend relevant images to the user. The recommendation algorithm is detailed, which is based on relevance feedback techniques

    Energy-efficient real estate or how it is perceived by potential homebuyers in four Latin American countries

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    This article analyses how energy efficiency regulatory frameworks have been developed in Argentina, Brazil, Mexico and Chile, within a context of developing countries, and it discusses if this context has been able to influence a culture of buildings’ energy efficiency in consumers. An online survey was applied to consumers who wanted to buy a house, aiming to understand their position regarding sustainability, and the role of the state versus the individual role, among other issues. The aim of the study is to identify consumer’s perception of energy efficiency and sustainability to promote a future research agenda in the Latin American context. In general, consumers value sustainability, except when they are presented in opposition to economic growth and social protection. However, it is possible to identify differences between Chile, with an established neoliberal economy, and countries that have economies in transition. Indeed, Argentina and Brazil show differences in terms of the role of the State, or the thermal comfort, which is considered a matter of habits rather than a mere technological problem. For driving more sustainable behaviours, consumers should be engaged in the implementation of these standards, creating a twofold process including homebuyers on one hand and mandatory requirements on the other.Peer ReviewedPostprint (published version

    Data Driven Dense 3D Facial Reconstruction From 3D Skull Shape

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    Indiana University-Purdue University Indianapolis (IUPUI)This thesis explores a data driven machine learning based solution for Facial reconstruction from three dimensional (3D) skull shape for recognizing or identifying unknown subjects during forensic investigation. With over 8000 unidentified bodies during the past 3 decades, facial reconstruction of disintegrated bodies in helping with identification has been a critical issue for forensic practitioners. Historically, clay modelling has been used for facial reconstruction that not only requires an expert in the field but also demands a substantial amount of time for modelling, even after acquiring the skull model. Such manual reconstruction typically takes from a month to over 3 months of time and effort. The solution presented in this thesis uses 3D Cone Beam Computed Tomography (CBCT) data collected from many people to build a model of the relationship of facial skin to skull bone over a dense set of locations on the face. It then uses this skin-to-bone relationship model learned from the data to reconstruct the predicted face model from a skull shape of an unknown subject. The thesis also extends the algorithm in a way that could help modify the reconstructed face model interactively to account for the effects of age or weight. This uses the predicted face model as a starting point and creates different hypotheses of the facial appearances for different physical attributes. Attributes like age and body mass index (BMI) are used to show the physical facial appearance changes with the help of a tool we constructed. This could improve the identification process. The thesis also presents a methods designed for testing and validating the facial reconstruction algorithm
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