988 resultados para digital object identifier
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The integration of the human brain with computers is an interesting new area of applied neuroscience, where one application is replacement of a person"s real body by a virtual representation. Here we demonstrate that a virtual limb can be made to feel part of your body if appropriate multisensory correlations are provided. We report an illusion that is invoked through tactile stimulation on a person"s hidden real right hand with synchronous virtual visual stimulation on an aligned 3D stereo virtual arm projecting horizontally out of their shoulder. An experiment with 21 male participants showed displacement of ownership towards the virtual hand, as illustrated by questionnaire responses and proprioceptive drift. A control experiment with asynchronous tapping was carried out with a different set of 20 male participants who did not experience the illusion. After 5 min of stimulation the virtual arm rotated. Evidence suggests that the extent of the illusion was also correlated with the degree of muscle activity onset in the right arm as measured by EMG during this period that the arm was rotating, for the synchronous but not the asynchronous condition. A completely virtual object can therefore be experienced as part of one"s self, which opens up the possibility that an entire virtual body could be felt as one"s own in future virtual reality applications or online games, and be an invaluable tool for the understanding of the brain mechanisms underlying body ownership.
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The number of digital images has been increasing exponentially in the last few years. People have problems managing their image collections and finding a specific image. An automatic image categorization system could help them to manage images and find specific images. In this thesis, an unsupervised visual object categorization system was implemented to categorize a set of unknown images. The system is unsupervised, and hence, it does not need known images to train the system which needs to be manually obtained. Therefore, the number of possible categories and images can be huge. The system implemented in the thesis extracts local features from the images. These local features are used to build a codebook. The local features and the codebook are then used to generate a feature vector for an image. Images are categorized based on the feature vectors. The system is able to categorize any given set of images based on the visual appearance of the images. Images that have similar image regions are grouped together in the same category. Thus, for example, images which contain cars are assigned to the same cluster. The unsupervised visual object categorization system can be used in many situations, e.g., in an Internet search engine. The system can categorize images for a user, and the user can then easily find a specific type of image.
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The large and growing number of digital images is making manual image search laborious. Only a fraction of the images contain metadata that can be used to search for a particular type of image. Thus, the main research question of this thesis is whether it is possible to learn visual object categories directly from images. Computers process images as long lists of pixels that do not have a clear connection to high-level semantics which could be used in the image search. There are various methods introduced in the literature to extract low-level image features and also approaches to connect these low-level features with high-level semantics. One of these approaches is called Bag-of-Features which is studied in the thesis. In the Bag-of-Features approach, the images are described using a visual codebook. The codebook is built from the descriptions of the image patches using clustering. The images are described by matching descriptions of image patches with the visual codebook and computing the number of matches for each code. In this thesis, unsupervised visual object categorisation using the Bag-of-Features approach is studied. The goal is to find groups of similar images, e.g., images that contain an object from the same category. The standard Bag-of-Features approach is improved by using spatial information and visual saliency. It was found that the performance of the visual object categorisation can be improved by using spatial information of local features to verify the matches. However, this process is computationally heavy, and thus, the number of images must be limited in the spatial matching, for example, by using the Bag-of-Features method as in this study. Different approaches for saliency detection are studied and a new method based on the Hessian-Affine local feature detector is proposed. The new method achieves comparable results with current state-of-the-art. The visual object categorisation performance was improved by using foreground segmentation based on saliency information, especially when the background could be considered as clutter.
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Surface area (SA) of poultry is an important parameter for heat and mass transfer calculations. Optical approaches, such as the moiré technique (MT), are non-destructive, result in accuracy and speed gains, and preserve the object integrity. The objective of this research was to develop and validate a new protocol for estimating the surface area (SA) of broiler chickens based on the MT. Sixty-six Ross breed broiler chickens (twenty-seven male, thirty-nine female, ages spanning all growth phases) were used in this study. The dimensions (length, width and height) and body mass of randomly selected broiler chickens were evaluated in the laboratory. Chickens were illuminated by a light source, and grids were projected onto the chickens to allow their shape to be determined and recorded. Next, the skin and feathers of the chickens were removed to allow SA to be determined by conventional means. These measurements were then used for calibration and validation. The MT for image analysis was a reliable means of evaluating the three-dimensional shape and SA of broiler chickens. This technique, which is neither invasive nor destructive, is a good alternative to the conventional destructive methods.
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The usage of digital content, such as video clips and images, has increased dramatically during the last decade. Local image features have been applied increasingly in various image and video retrieval applications. This thesis evaluates local features and applies them to image and video processing tasks. The results of the study show that 1) the performance of different local feature detector and descriptor methods vary significantly in object class matching, 2) local features can be applied in image alignment with superior results against the state-of-the-art, 3) the local feature based shot boundary detection method produces promising results, and 4) the local feature based hierarchical video summarization method shows promising new new research direction. In conclusion, this thesis presents the local features as a powerful tool in many applications and the imminent future work should concentrate on improving the quality of the local features.
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Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014
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Poster at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014
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Human beings have always strived to preserve their memories and spread their ideas. In the beginning this was always done through human interpretations, such as telling stories and creating sculptures. Later, technological progress made it possible to create a recording of a phenomenon; first as an analogue recording onto a physical object, and later digitally, as a sequence of bits to be interpreted by a computer. By the end of the 20th century technological advances had made it feasible to distribute media content over a computer network instead of on physical objects, thus enabling the concept of digital media distribution. Many digital media distribution systems already exist, and their continued, and in many cases increasing, usage is an indicator for the high interest in their future enhancements and enriching. By looking at these digital media distribution systems, we have identified three main areas of possible improvement: network structure and coordination, transport of content over the network, and the encoding used for the content. In this thesis, our aim is to show that improvements in performance, efficiency and availability can be done in conjunction with improvements in software quality and reliability through the use of formal methods: mathematical approaches to reasoning about software so that we can prove its correctness, together with the desirable properties. We envision a complete media distribution system based on a distributed architecture, such as peer-to-peer networking, in which different parts of the system have been formally modelled and verified. Starting with the network itself, we show how it can be formally constructed and modularised in the Event-B formalism, such that we can separate the modelling of one node from the modelling of the network itself. We also show how the piece selection algorithm in the BitTorrent peer-to-peer transfer protocol can be adapted for on-demand media streaming, and how this can be modelled in Event-B. Furthermore, we show how modelling one peer in Event-B can give results similar to simulating an entire network of peers. Going further, we introduce a formal specification language for content transfer algorithms, and show that having such a language can make these algorithms easier to understand. We also show how generating Event-B code from this language can result in less complexity compared to creating the models from written specifications. We also consider the decoding part of a media distribution system by showing how video decoding can be done in parallel. This is based on formally defined dependencies between frames and blocks in a video sequence; we have shown that also this step can be performed in a way that is mathematically proven correct. Our modelling and proving in this thesis is, in its majority, tool-based. This provides a demonstration of the advance of formal methods as well as their increased reliability, and thus, advocates for their more wide-spread usage in the future.
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Current research describes digital innovation largely similar to product innovation. Digital innovation is seen as an object of coherent activities, however in reality digital innovation results from convergence of variant technologies and those related actors with versatile business goals. To account for the dynamic nature of digital innovation, this study applies a service perspective to digital innovation. The purpose of the study is to understand how digital innovation emerges within a service ecosystem for autonomous shipping. The sub-objectives of this study are to 1) identify what factors motivate and demotivate actors to integrate resources for autonomous shipping, 2) explore the key technology areas to be integrated to realise the autonomous shipping concept, and 3) suggest how the technology areas are combined for mutual value creation within a service eco-system for autonomous shipping. Insights from autonomous driving were also included. This study draws on literatures on service innovation and service-dominant logic. The research was conducted as a qualitative exploratory case study. The data comprise interviews of 18 marine and automotive industry experts, 4 workshops, 4 seminars, and observations as well as various secondary data sources. The findings revealed that the key actors have versatile motivations regarding autonomous shipping. These varied from opportunities for single applications to occupying a central role in an autonomous technology platform. Thus, autonomous shipping can be seen as an umbrella concept comprising multiple levels. In technical terms, the development of the concept of autonomous shipping is largely based on combining existing technology solutions, which are gradually integrated towards more systemic entities comprising areas of the autonomous shipping concept. This study argues that a service perspective embraces the inherently complex and dynamic nature of digital innovation. This is captured in the developed research framework that describes digital innovation emerging on different levels of interaction: 1. strategic relationships for new solutions, 2. new local networks for technology platforms, and 3. global networks for new markets. The framework shows how the business models and motivations of digital innovation actors feed the emergence of digital innovation in overlapping service ecosystems that together comprise an innovation ecosystem for autonomous technologies. Digital innovation managers will benefit from seeing their businesses as part of a larger ecosystem of value co-creating actors. In orchestrating digital innovation within a service ecosystem, it is suggested that managers consider the resources, roles and institutions within the ecosystem. Finally, as autonomous shipping is at its infancy, the topic provides a number of interesting avenues for future research.
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We propose a probabilistic object classifier for outdoor scene analysis as a first step in solving the problem of scene context generation. The method begins with a top-down control, which uses the previously learned models (appearance and absolute location) to obtain an initial pixel-level classification. This information provides us the core of objects, which is used to acquire a more accurate object model. Therefore, their growing by specific active regions allows us to obtain an accurate recognition of known regions. Next, a stage of general segmentation provides the segmentation of unknown regions by a bottom-strategy. Finally, the last stage tries to perform a region fusion of known and unknown segmented objects. The result is both a segmentation of the image and a recognition of each segment as a given object class or as an unknown segmented object. Furthermore, experimental results are shown and evaluated to prove the validity of our proposal
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Given a set of images of scenes containing different object categories (e.g. grass, roads) our objective is to discover these objects in each image, and to use this object occurrences to perform a scene classification (e.g. beach scene, mountain scene). We achieve this by using a supervised learning algorithm able to learn with few images to facilitate the user task. We use a probabilistic model to recognise the objects and further we classify the scene based on their object occurrences. Experimental results are shown and evaluated to prove the validity of our proposal. Object recognition performance is compared to the approaches of He et al. (2004) and Marti et al. (2001) using their own datasets. Furthermore an unsupervised method is implemented in order to evaluate the advantages and disadvantages of our supervised classification approach versus an unsupervised one
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A new method for the automated selection of colour features is described. The algorithm consists of two stages of processing. In the first, a complete set of colour features is calculated for every object of interest in an image. In the second stage, each object is mapped into several n-dimensional feature spaces in order to select the feature set with the smallest variables able to discriminate the remaining objects. The evaluation of the discrimination power for each concrete subset of features is performed by means of decision trees composed of linear discrimination functions. This method can provide valuable help in outdoor scene analysis where no colour space has been demonstrated as being the most suitable. Experiment results recognizing objects in outdoor scenes are reported
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Este trabajo de investigación tuvo como objetivo principal describir la construcción narrativa de las políticas públicas sobre tecnologías de la información en el gobierno de Juan Manuel Santos en los años 2012 y 2013 en la prensa de referencia en Colombia, específicamente en El Espectador y El Tiempo. Para alcanzar este objetivo la pregunta que se planteo fue ¿cómo está conformada la construcción narrativa del discurso de políticas públicas en la prensa sobre tecnologías de la información y comunicaciones? En consiguiente se hizo una organización de información referente al objeto de estudio, a través de un método de investigación empleado por Mary Jane Spink llamado líneas narrativas. El cual le permitió a la investigación la identificación de relatos, en donde se estudia la narrativa de una política TIC emergente y da como resultado líneas narrativas.
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Flood modelling of urban areas is still at an early stage, partly because until recently topographic data of sufficiently high resolution and accuracy have been lacking in urban areas. However, Digital Surface Models (DSMs) generated from airborne scanning laser altimetry (LiDAR) having sub-metre spatial resolution have now become available, and these are able to represent the complexities of urban topography. The paper describes the development of a LiDAR post-processor for urban flood modelling based on the fusion of LiDAR and digital map data. The map data are used in conjunction with LiDAR data to identify different object types in urban areas, though pattern recognition techniques are also employed. Post-processing produces a Digital Terrain Model (DTM) for use as model bathymetry, and also a friction parameter map for use in estimating spatially-distributed friction coefficients. In vegetated areas, friction is estimated from LiDAR-derived vegetation height, and (unlike most vegetation removal software) the method copes with short vegetation less than ~1m high, which may occupy a substantial fraction of even an urban floodplain. The DTM and friction parameter map may also be used to help to generate an unstructured mesh of a vegetated urban floodplain for use by a 2D finite element model. The mesh is decomposed to reflect floodplain features having different frictional properties to their surroundings, including urban features such as buildings and roads as well as taller vegetation features such as trees and hedges. This allows a more accurate estimation of local friction. The method produces a substantial node density due to the small dimensions of many urban features.
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A major infrastructure project is used to investigate the role of digital objects in the coordination of engineering design work. From a practice-based perspective, research emphasizes objects as important in enabling cooperative knowledge work and knowledge sharing. The term ‘boundary object’ has become used in the analysis of mutual and reciprocal knowledge sharing around physical and digital objects. The aim is to extend this work by analysing the introduction of an extranet into the public–private partnership project used to construct a new motorway. Multiple categories of digital objects are mobilized in coordination across heterogeneous, cross-organizational groups. The main findings are that digital objects provide mechanisms for accountability and control, as well as for mutual and reciprocal knowledge sharing; and that different types of objects are nested, forming a digital infrastructure for project delivery. Reconceptualizing boundary objects as a digital infrastructure for delivery has practical implications for management practices on large projects and for the use of digital tools, such as building information models, in construction. It provides a starting point for future research into the changing nature of digitally enabled coordination in project-based work.