4,722 research outputs found

    Thermal infrared video stabilization for aerial monitoring of active wildfires

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    Measuring wildland fire behavior is essential for fire science and fire management. Aerial thermal infrared (TIR) imaging provides outstanding opportunities to acquire such information remotely. Variables such as fire rate of spread (ROS), fire radiative power (FRP), and fireline intensity may be measured explicitly both in time and space, providing the necessary data to study the response of fire behavior to weather, vegetation, topography, and firefighting efforts. However, raw TIR imagery acquired by unmanned aerial vehicles (UAVs) requires stabilization and georeferencing before any other processing can be performed. Aerial video usually suffers from instabilities produced by sensor movement. This problem is especially acute near an active wildfire due to fire-generated turbulence. Furthermore, the nature of fire TIR video presents some specific challenges that hinder robust interframe registration. Therefore, this article presents a software-based video stabilization algorithm specifically designed for TIR imagery of forest fires. After a comparative analysis of existing image registration algorithms, the KAZE feature-matching method was selected and accompanied by pre- and postprocessing modules. These included foreground histogram equalization and a multireference framework designed to increase the algorithm's robustness in the presence of missing or faulty frames. The performance of the proposed algorithm was validated in a total of nine video sequences acquired during field fire experiments. The proposed algorithm yielded a registration accuracy between 10 and 1000x higher than other tested methods, returned 10x more meaningful feature matches, and proved robust in the presence of faulty video frames. The ability to automatically cancel camera movement for every frame in a video sequence solves a key limitation in data processing pipelines and opens the door to a number of systematic fire behavior experimental analyses. Moreover, a completely automated process supports the development of decision support tools that can operate in real time during an emergency

    3-D model construction using range and image data

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    This paper deals with the automated creation of geometric and photometric correct 3-D models of the world. Those models can be used for virtual reality, tele-presence, digital cinematography and urban planning applications. The combination of range (dense depth estimates) and image sensing (color information) provides data-sets which allow us to create geometrically correct, photorealistic models of high quality. The 3-D models are first built from range data using a volumetric set intersection method previously developed by us. Photometry can be mapped onto these models by registering features from both the 3-D and 2-D data sets. Range data segmentation algorithms have been developed to identify planar regions, determine linear features from planar intersections that can serve as features for registration with 2-D imagery lines, and reduce the overall complexity of the models. Results are shown for building models of large buildings on our campus using real data acquired from multiple sensors

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

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    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

    Imaging methods for understanding and improving visual training in the geosciences

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    Experience in the field is a critical educational component of every student studying geology. However, it is typically difficult to ensure that every student gets the necessary experience because of monetary and scheduling limitations. Thus, we proposed to create a virtual field trip based off of an existing 10-day field trip to California taken as part of an undergraduate geology course at the University of Rochester. To assess the effectiveness of this approach, we also proposed to analyze the learning and observation processes of both students and experts during the real and virtual field trips. At sites intended for inclusion in the virtual field trip, we captured gigapixel resolution panoramas by taking hundreds of images using custom built robotic imaging systems. We gathered data to analyze the learning process by fitting each geology student and expert with a portable eye- tracking system that records a video of their eye movements and a video of the scene they are observing. An important component of analyzing the eye-tracking data requires mapping the gaze of each observer into a common reference frame. We have made progress towards developing a software tool that helps automate this procedure by using image feature tracking and registration methods to map the scene video frames from each eye-tracker onto a reference panorama for each site. For the purpose of creating a virtual field trip, we have a large scale semi-immersive display system that consists of four tiled projectors, which have been colorimetrically and photometrically calibrated, and a curved widescreen display surface. We use this system to present the previously captured panoramas, which simulates the experience of visiting the sites in person. In terms of broader geology education and outreach, we have created an interactive website that uses Google Earth as the interface for visually exploring the panoramas captured for each site

    Cross-View Visual Geo-Localization for Outdoor Augmented Reality

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    Precise estimation of global orientation and location is critical to ensure a compelling outdoor Augmented Reality (AR) experience. We address the problem of geo-pose estimation by cross-view matching of query ground images to a geo-referenced aerial satellite image database. Recently, neural network-based methods have shown state-of-the-art performance in cross-view matching. However, most of the prior works focus only on location estimation, ignoring orientation, which cannot meet the requirements in outdoor AR applications. We propose a new transformer neural network-based model and a modified triplet ranking loss for joint location and orientation estimation. Experiments on several benchmark cross-view geo-localization datasets show that our model achieves state-of-the-art performance. Furthermore, we present an approach to extend the single image query-based geo-localization approach by utilizing temporal information from a navigation pipeline for robust continuous geo-localization. Experimentation on several large-scale real-world video sequences demonstrates that our approach enables high-precision and stable AR insertion.Comment: IEEE VR 202

    Augmented reality applications for cultural heritage using Kinect

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    AbstractThis paper explores the use of data from the Kinect sensor for performing augmented reality, with emphasis on cultural heritage applications. It is shown that the combination of depth and image correspondences from the Kinect can yield a reliable estimate of the location and pose of the camera, though noise from the depth sensor introduces an unpleasant jittering of the rendered view. Kalman filtering of the camera position was found to yield a much more stable view. Results show that the system is accurate enough for in situ augmented reality applications. Skeleton tracking using Kinect data allows the appearance of participants to be augmented, and together these facilitate the development of cultural heritage applications.</jats:p
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