An Approach Of Automatic Reconstruction Of Building Models For Virtual Cities From Open Resources

Along with the ever-increasing popularity of virtual reality technology in recent years, 3D city models have been used in different applications, such as urban planning, disaster management, tourism, entertainment, and video games. Currently, those models are mainly reconstructed from access-restricted data sources such as LiDAR point clouds, airborne images, satellite images, and UAV (uncrewed air vehicle) images with a focus on structural illustration of buildings’ contours and layouts. To help make 3D models closer to their real-life counterparts, this thesis research proposes a new approach for the automatic reconstruction of building models from open resources. In this approach, first, building shapes are reconstructed by using the structural and geographic information retrievable from the open repository of OpenStreetMap (OSM). Later, images available from the street view of Google maps are used to extract information of the exterior appearance of buildings for texture mapping onto their boundaries. The constructed 3D environment is used as prior knowledge for the navigation purposes in a self-driving car. The static objects from the 3D model are compared with the real-time images of static objects to reduce the computation time by eliminating them from the detection proces

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

GEO-REFERENCED VIDEO RETRIEVAL: TEXT ANNOTATION AND SIMILARITY SEARCH

Ph.DDOCTOR OF PHILOSOPH

SPATIAL SENSOR DATA PROCESSING AND ANALYSIS FOR MOBILE MEDIA APPLICATIONS

Ph.DDOCTOR OF PHILOSOPH

Share Our Cultural Heritage (SOCH): Worldwide 3D Heritage Reconstruction and Visualization via Web and Mobile GIS

Despite being of paramount importance to humanity, tangible cultural heritage is often at risk from natural and anthropogenic threats worldwide. As a result, heritage discovery and conservation remain a huge challenge for both developed and developing countries, with heritage sites often inadequately cared for, be it due to a lack of resources, nonrecognition of the value by local people or authorities, human conflict, or some other reason. This paper presents an online geo-crowdsourcing system, termed Share Our Cultural Heritage (SOCH), which can be utilized for large-scale heritage documentation and sharing. Supported by web and mobile GIS, cultural heritage data such as textual stories, locations, and images can be acquired via portable devices. These data are georeferenced and presented to the public via web-mapping. Using photogrammetric modelling, acquired images are used to reconstruct heritage structures or artefacts into 3D digital models, which are then visualized on the SOCH web interface to enable public interaction. This end-to-end system incubates an online virtual community to encourage public engagement, raise awareness, and stimulate cultural heritage ownership. It also provides valuable resources for cultural heritage exploitation, management, education, and monitoring over time

REAL TIME ASSISTANCE IN PHOTOGRAPHY USING SOCIAL MEDIA

Ph.DDOCTOR OF PHILOSOPH

Geo-Information Harvesting from Social Media Data

As unconventional sources of geo-information, massive imagery and text messages from open platforms and social media form a temporally quasi-seamless, spatially multi-perspective stream, but with unknown and diverse quality. Due to its complementarity to remote sensing data, geo-information from these sources offers promising perspectives, but harvesting is not trivial due to its data characteristics. In this article, we address key aspects in the field, including data availability, analysis-ready data preparation and data management, geo-information extraction from social media text messages and images, and the fusion of social media and remote sensing data. We then showcase some exemplary geographic applications. In addition, we present the first extensive discussion of ethical considerations of social media data in the context of geo-information harvesting and geographic applications. With this effort, we wish to stimulate curiosity and lay the groundwork for researchers who intend to explore social media data for geo-applications. We encourage the community to join forces by sharing their code and data.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin

Fusing Multimedia Data Into Dynamic Virtual Environments

In spite of the dramatic growth of virtual and augmented reality (VR and AR) technology, content creation for immersive and dynamic virtual environments remains a significant challenge. In this dissertation, we present our research in fusing multimedia data, including text, photos, panoramas, and multi-view videos, to create rich and compelling virtual environments. First, we present Social Street View, which renders geo-tagged social media in its natural geo-spatial context provided by 360° panoramas. Our system takes into account visual saliency and uses maximal Poisson-disc placement with spatiotemporal filters to render social multimedia in an immersive setting. We also present a novel GPU-driven pipeline for saliency computation in 360° panoramas using spherical harmonics (SH). Our spherical residual model can be applied to virtual cinematography in 360° videos. We further present Geollery, a mixed-reality platform to render an interactive mirrored world in real time with three-dimensional (3D) buildings, user-generated content, and geo-tagged social media. Our user study has identified several use cases for these systems, including immersive social storytelling, experiencing the culture, and crowd-sourced tourism. We next present Video Fields, a web-based interactive system to create, calibrate, and render dynamic videos overlaid on 3D scenes. Our system renders dynamic entities from multiple videos, using early and deferred texture sampling. Video Fields can be used for immersive surveillance in virtual environments. Furthermore, we present VRSurus and ARCrypt projects to explore the applications of gestures recognition, haptic feedback, and visual cryptography for virtual and augmented reality. Finally, we present our work on Montage4D, a real-time system for seamlessly fusing multi-view video textures with dynamic meshes. We use geodesics on meshes with view-dependent rendering to mitigate spatial occlusion seams while maintaining temporal consistency. Our experiments show significant enhancement in rendering quality, especially for salient regions such as faces. We believe that Social Street View, Geollery, Video Fields, and Montage4D will greatly facilitate several applications such as virtual tourism, immersive telepresence, and remote education

Summarization from Multiple User Generated Videos in Geo-Space

Ph.DDOCTOR OF PHILOSOPH