Digital Image Access & Retrieval

The 33th Annual Clinic on Library Applications of Data Processing, held at the University of Illinois at Urbana-Champaign in March of 1996, addressed the theme of "Digital Image Access & Retrieval." The papers from this conference cover a wide range of topics concerning digital imaging technology for visual resource collections. Papers covered three general areas: (1) systems, planning, and implementation; (2) automatic and semi-automatic indexing; and (3) preservation with the bulk of the conference focusing on indexing and retrieval.published or submitted for publicatio

Presentation of multiple GEO-referenced videos

Master'sMASTER OF SCIENC

Linking Spatial Video and GIS

Spatial Video is any form of geographically referenced videographic data. The forms in which it is acquired, stored and used vary enormously; as does the standard of accuracy in the spatial data and the quality of the video footage. This research deals with a specific form of Spatial Video where these data have been captured from a moving road-network survey vehicle. The spatial data are GPS sentences while the video orientation is approximately orthogonal and coincident with the direction of travel. GIS that use these data are usually bespoke standalone systems or third party extensions to existing platforms. They specialise in using the video as a visual enhancement with limited spatial functionality and interoperability. While enormous amounts of these data exist, they do not have a generalised, cross-platform spatial data structure that is suitable for use within a GIS. The objectives of this research have been to define, develop and implement a novel Spatial Video data structure and demonstrate how this can achieve a spatial approach to the study of video. This data structure is called a Viewpoint and represents the capture location and geographical extent of each video frame. It is generalised to represent any form or format of Spatial Video. It is shown how a Viewpoint improves on existing data structure methodologies and how it can be theoretically defined in 3D space. A 2D implementation is then developed where Viewpoints are constructed from the spatial and camera parameters of each survey in the study area. A number of problems are defined and solutions provided towards the implementation of a post-processing system to calculate, index and store each video frame Viewpoint in a centralised spatial database. From this spatial database a number of geospatial analysis approaches are demonstrated that represent novel ways of using and studying Spatial Video based on the Viewpoint data structure. Also, a unique application is developed where the Viewpoints are used as a spatial control to dynamically access and play video in a location aware system. While video has been to date largely ignored as a GIS spatial data source; it is shown through this novel Viewpoint implementation and the geospatial analysis demonstrations that this need not be the case anymore

CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research

MusA: Using Indoor Positioning and Navigation to Enhance Cultural Experiences in a museum

In recent years there has been a growing interest into the use of multimedia mobile guides in museum environments. Mobile devices have the capabilities to detect the user context and to provide pieces of information suitable to help visitors discovering and following the logical and emotional connections that develop during the visit. In this scenario, location based services (LBS) currently represent an asset, and the choice of the technology to determine users' position, combined with the definition of methods that can effectively convey information, become key issues in the design process. In this work, we present MusA (Museum Assistant), a general framework for the development of multimedia interactive guides for mobile devices. Its main feature is a vision-based indoor positioning system that allows the provision of several LBS, from way-finding to the contextualized communication of cultural contents, aimed at providing a meaningful exploration of exhibits according to visitors' personal interest and curiosity. Starting from the thorough description of the system architecture, the article presents the implementation of two mobile guides, developed to respectively address adults and children, and discusses the evaluation of the user experience and the visitors' appreciation of these application

Collaborative geographic visualization

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para a obtenção do grau de Mestre em Engenharia do Ambiente, perfil Gestão e Sistemas AmbientaisThe present document is a revision of essential references to take into account when developing ubiquitous Geographical Information Systems (GIS) with collaborative visualization purposes. Its chapters focus, respectively, on general principles of GIS, its multimedia components and ubiquitous practices; geo-referenced information visualization and its graphical components of virtual and augmented reality; collaborative environments, its technological requirements, architectural specificities, and models for collective information management; and some final considerations about the future and challenges of collaborative visualization of GIS in ubiquitous environment

Geospatial Information Research: State of the Art, Case Studies and Future Perspectives

Geospatial information science (GI science) is concerned with the development and application of geodetic and information science methods for modeling, acquiring, sharing, managing, exploring, analyzing, synthesizing, visualizing, and evaluating data on spatio-temporal phenomena related to the Earth. As an interdisciplinary scientific discipline, it focuses on developing and adapting information technologies to understand processes on the Earth and human-place interactions, to detect and predict trends and patterns in the observed data, and to support decision making. The authors – members of DGK, the Geoinformatics division, as part of the Committee on Geodesy of the Bavarian Academy of Sciences and Humanities, representing geodetic research and university teaching in Germany – have prepared this paper as a means to point out future research questions and directions in geospatial information science. For the different facets of geospatial information science, the state of art is presented and underlined with mostly own case studies. The paper thus illustrates which contributions the German GI community makes and which research perspectives arise in geospatial information science. The paper further demonstrates that GI science, with its expertise in data acquisition and interpretation, information modeling and management, integration, decision support, visualization, and dissemination, can help solve many of the grand challenges facing society today and in the future

Public Commons for Geospatial Data: A Conceptual Model

A wide variety of spatial data collection efforts are ongoing throughout local, state and federal agencies, private firms and non-profit organizations. Each effort is established for a different purpose but organizations and individuals often collect and maintain the same or similar information. The United States federal government has undertaken many initiatives such as the National Spatial Data Infrastructure, the National Map and Geospatial One-Stop to reduce duplicative spatial data collection and promote the coordinated use, sharing, and dissemination of spatial data nationwide. A key premise in most of these initiatives is that no national government will be able to gather and maintain more than a small percentage of the geographic data that users want and desire. Thus, national initiatives depend typically on the cooperation of those already gathering spatial data and those using GIs to meet specific needs to help construct and maintain these spatial data infrastructures and geo-libraries for their nations (Onsrud 2001). Some of the impediments to widespread spatial data sharing are well known from directly asking GIs data producers why they are not currently involved in creating datasets that are of common or compatible formats, documenting their datasets in a standardized metadata format or making their datasets more readily available to others through Data Clearinghouses or geo-libraries. The research described in this thesis addresses the impediments to wide-scale spatial data sharing faced by GIs data producers and explores a new conceptual data-sharing approach, the Public Commons for Geospatial Data, that supports user-friendly metadata creation, open access licenses, archival services and documentation of parent lineage of the contributors and value- adders of digital spatial data sets

Visual Geo-Localization and Location-Aware Image Understanding

Geo-localization is the problem of discovering the location where an image or video was captured. Recently, large scale geo-localization methods which are devised for ground-level imagery and employ techniques similar to image matching have attracted much interest. In these methods, given a reference dataset composed of geo-tagged images, the problem is to estimate the geo-location of a query by finding its matching reference images. In this dissertation, we address three questions central to geo-spatial analysis of ground-level imagery: 1) How to geo-localize images and videos captured at unknown locations? 2) How to refine the geo-location of already geo-tagged data? 3) How to utilize the extracted geo-tags? We present a new framework for geo-locating an image utilizing a novel multiple nearest neighbor feature matching method using Generalized Minimum Clique Graphs (GMCP). First, we extract local features (e.g., SIFT) from the query image and retrieve a number of nearest neighbors for each query feature from the reference data set. Next, we apply our GMCP-based feature matching to select a single nearest neighbor for each query feature such that all matches are globally consistent. Our approach to feature matching is based on the proposition that the first nearest neighbors are not necessarily the best choices for finding correspondences in image matching. Therefore, the proposed method considers multiple reference nearest neighbors as potential matches and selects the correct ones by enforcing the consistency among their global features (e.g., GIST) using GMCP. Our evaluations using a new data set of 102k Street View images shows the proposed method outperforms the state-of-the-art by 10 percent. Geo-localization of images can be extended to geo-localization of a video. We have developed a novel method for estimating the geo-spatial trajectory of a moving camera with unknown intrinsic parameters in a city-scale. The proposed method is based on a three step process: 1) individual geo-localization of video frames using Street View images to obtain the likelihood of the location (latitude and longitude) given the current observation, 2) Bayesian tracking to estimate the frame location and video\u27s temporal evolution using previous state probabilities and current likelihood, and 3) applying a novel Minimum Spanning Trees based trajectory reconstruction to eliminate trajectory loops or noisy estimations. Thus far, we have assumed reliable geo-tags for reference imagery are available through crowdsourcing. However, crowdsourced images are well known to suffer from the acute shortcoming of having inaccurate geo-tags. We have developed the first method for refinement of GPS-tags which automatically discovers the subset of corrupted geo-tags and refines them. We employ Random Walks to discover the uncontaminated subset of location estimations and robustify Random Walks with a novel adaptive damping factor that conforms to the level of noise in the input. In location-aware image understanding, we are interested in improving the image analysis by putting it in the right geo-spatial context. This approach is of particular importance as the majority of cameras and mobile devices are now being equipped with GPS chips. Therefore, developing techniques which can leverage the geo-tags of images for improving the performance of traditional computer vision tasks is of particular interest. We have developed a location-aware multimodal approach which incorporates business directories, textual information, and web images to identify businesses in a geo-tagged query image

The Exploitation of Data from Remote and Human Sensors for Environment Monitoring in the SMAT Project

In this paper, we outline the functionalities of a system that integrates and controls a fleet of Unmanned Aircraft Vehicles (UAVs). UAVs have a set of payload sensors employed for territorial surveillance, whose outputs are stored in the system and analysed by the data exploitation functions at different levels. In particular, we detail the second level data exploitation function whose aim is to improve the sensors data interpretation in the post-mission activities. It is concerned with the mosaicking of the aerial images and the cartography enrichment by human sensors—the social media users. We also describe the software architecture for the development of a mash-up (the integration of information and functionalities coming from the Web) and the possibility of using human sensors in the monitoring of the territory, a field in which, traditionally, the involved sensors were only the hardware ones.JRC.H.6-Digital Earth and Reference Dat