963 research outputs found

    Media aesthetics based multimedia storytelling.

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    Since the earliest of times, humans have been interested in recording their life experiences, for future reference and for storytelling purposes. This task of recording experiences --i.e., both image and video capture-- has never before in history been as easy as it is today. This is creating a digital information overload that is becoming a great concern for the people that are trying to preserve their life experiences. As high-resolution digital still and video cameras become increasingly pervasive, unprecedented amounts of multimedia, are being downloaded to personal hard drives, and also uploaded to online social networks on a daily basis. The work presented in this dissertation is a contribution in the area of multimedia organization, as well as automatic selection of media for storytelling purposes, which eases the human task of summarizing a collection of images or videos in order to be shared with other people. As opposed to some prior art in this area, we have taken an approach in which neither user generated tags nor comments --that describe the photographs, either in their local or on-line repositories-- are taken into account, and also no user interaction with the algorithms is expected. We take an image analysis approach where both the context images --e.g. images from online social networks to which the image stories are going to be uploaded--, and the collection images --i.e., the collection of images or videos that needs to be summarized into a story--, are analyzed using image processing algorithms. This allows us to extract relevant metadata that can be used in the summarization process. Multimedia-storytellers usually follow three main steps when preparing their stories: first they choose the main story characters, the main events to describe, and finally from these media sub-groups, they choose the media based on their relevance to the story as well as based on their aesthetic value. Therefore, one of the main contributions of our work has been the design of computational models --both regression based, as well as classification based-- that correlate well with human perception of the aesthetic value of images and videos. These computational aesthetics models have been integrated into automatic selection algorithms for multimedia storytelling, which are another important contribution of our work. A human centric approach has been used in all experiments where it was feasible, and also in order to assess the final summarization results, i.e., humans are always the final judges of our algorithms, either by inspecting the aesthetic quality of the media, or by inspecting the final story generated by our algorithms. We are aware that a perfect automatically generated story summary is very hard to obtain, given the many subjective factors that play a role in such a creative process; rather, the presented approach should be seen as a first step in the storytelling creative process which removes some of the ground work that would be tedious and time consuming for the user. Overall, the main contributions of this work can be capitalized in three: (1) new media aesthetics models for both images and videos that correlate with human perception, (2) new scalable multimedia collection structures that ease the process of media summarization, and finally, (3) new media selection algorithms that are optimized for multimedia storytelling purposes.Postprint (published version

    Generic Object Detection and Segmentation for Real-World Environments

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    LifeLogging: personal big data

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    We have recently observed a convergence of technologies to foster the emergence of lifelogging as a mainstream activity. Computer storage has become significantly cheaper, and advancements in sensing technology allows for the efficient sensing of personal activities, locations and the environment. This is best seen in the growing popularity of the quantified self movement, in which life activities are tracked using wearable sensors in the hope of better understanding human performance in a variety of tasks. This review aims to provide a comprehensive summary of lifelogging, to cover its research history, current technologies, and applications. Thus far, most of the lifelogging research has focused predominantly on visual lifelogging in order to capture life details of life activities, hence we maintain this focus in this review. However, we also reflect on the challenges lifelogging poses to an information retrieval scientist. This review is a suitable reference for those seeking a information retrieval scientist’s perspective on lifelogging and the quantified self

    Developing a home monitoring system for patients with chronic liver disease using a smartphone

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    Liver disease is a growing problem in the UK, and one of the major causes of working-age premature death. Patients with advanced liver disease are typically admitted to hospital on multiple occasions, where they are stabilised before discharge. At home, there is little or no monitoring of their condition available, making it difficult to time additional treatment. Here, a system for non-invasive assessment of serum bilirubin level is proposed, based on imaging the white of the eye (sclera) using a smartphone. Elevated bilirubin level manifests as jaundice, and is a key indicator of overall liver function. Smartphone imaging makes the system low cost, portable and non-contact. An ambient subtraction technique based on subtracting data from flash/ no-flash image pairs is leveraged to account for variations in ambient light. The subtracted signal to noise ratio (SSNR) metric has been developed to ensure good image quality. Values falling below the experimentally-determined threshold of 3.4 trigger a warning to re-capture. To produce device-independent results, mapping approaches based on image metadata and colour chart images were compared. It was found that introducing a one-time calibration step of imaging a colour chart for each device leads to the best compatibility of results from different phones. In a clinical study at the Royal Free Hospital, London, over 100 sets of patient scleral images were captured with two different smartphones and paired clinical information was recorded. A filtering algorithm was developed to tackle the high density of blood vessels and specular reflection observed in the images, yielding a 94% success rate. Strong cross-sectional and longitudinal correlations of scleral yellowness and serum bilirubin level were found of 0.89 and 0.72 respectively (both p<0.001). When the proposed processing was applied, results from the two phones were demonstrated to be compatible. These results demonstrate the strong potential for the system as a monitoring tool

    Fine Art Pattern Extraction and Recognition

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    This is a reprint of articles from the Special Issue published online in the open access journal Journal of Imaging (ISSN 2313-433X) (available at: https://www.mdpi.com/journal/jimaging/special issues/faper2020)

    Annual Report: 2008

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    I submit herewith the annual report from the Agricultural and Forestry Experiment Station, School of Natural Resources and Agricultural Sciences, University of Alaska Fairbanks, for the period ending December 31, 2008. This is done in accordance with an act of Congress, approved March 2, 1887, entitled, “An act to establish agricultural experiment stations, in connection with the agricultural college established in the several states under the provisions of an act approved July 2, 1862, and under the acts supplementary thereto,” and also of the act of the Alaska Territorial Legislature, approved March 12, 1935, accepting the provisions of the act of Congress. The research reports are organized according to our strategic plan, which focuses on high-latitude soils, high-latitude agriculture, natural resources use and allocation, ecosystems management, and geographic information. These areas cross department and unit lines, linking them and unifying the research. We have also included in our financial statement information on the special grants we receive. These special grants allow us to provide research and outreach that is targeted toward economic development in Alaska. Research conducted by our graduate and undergraduate students plays an important role in these grants and the impact they make on Alaska.Financial statement -- Grants -- Students -- Research reports: Partners, Facilities, and Programs; Geographic Information; High-Latitude Agriculture; High-Latitude Soils, Management of Ecosystems; Natural Resources Use and Allocation; Index to Reports -- Publications -- Facult

    Center for Research on Sustainable Forests 2021 Annual Report

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    The Center for Research on Sustainable Forests (CRSF) and Cooperative Forestry Research Unit (CFRU) continued to move forward on multiple fronts with a particularly productive and rewarding FY18-19. This included leadership on several key new initiatives such as the Forest Climate Change Initiative (FCCI), Intelligent GeoSolutions (IGS), and a funded National Science Foundation (NSF) Track 2 EPSCoR grant (INSPIRES). This is in addition to ongoing leadership and support for important CRSF programs such as NSF’s Center for Advanced Forestry Systems (CAFS), the Northeastern Research Cooperative (NSRC), and FOR/Maine. In short, CRSF is on a bold upward trajectory that highlights its relevance and solid leadership with a rather bright future

    Center for Research on Sustainable Forests 2019 Annual Report

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    The Center for Research on Sustainable Forests (CRSF) and Cooperative Forestry Research Unit (CFRU) continued to move forward on multiple fronts with a particularly productive and rewarding FY18-19. This included leadership on several key new initiatives such as the Forest Climate Change Initiative (FCCI), Intelligent GeoSolutions (IGS), and a funded National Science Foundation (NSF) Track 2 EPSCoR grant (INSPIRES). This is in addition to ongoing leadership and support for important CRSF programs such as NSF’s Center for Advanced Forestry Systems (CAFS), the Northeastern Research Cooperative (NSRC), and FOR/Maine. In short, CRSF is on a bold upward trajectory that highlights its relevance and solid leadership with a rather bright future
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