Titlebook: Large-Scale Visual Geo-Localization; Amir R. Zamir,Asaad Hakeem,Richard Szeliski Book 2016 Springer International Publishing Switzerland 2

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Introduction to Large-Scale Visual Geo-localization,task. The majority of existing computer vision solutions for geo-localization are limited to highly-visited urban regions for which a significant amount of geo-tagged imagery is available, and therefore, do not scale well to large and ordinary geo-spatial regions. In this chapter, we provide an over

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Discovering Mid-level Visual Connections in Space and Timeing proposes to cluster visual patterns that capture more complex appearance than low-level blobs, corners, or oriented bars, without requiring any semantic labels. In particular, mid-level visual elements have recently been proposed as a new type of visual primitive, and have been shown to be usefu

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Where the Photos Were Taken: Location Prediction by Learning from Flickr Photosdevelop a principled machine learning model to estimate geographical locations of photos by modeling the relationship between location and the photo content. To build reliable geographical estimators, it is important to find distinguishable geographical clusters in the world. These clusters cover ge

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Cross-View Image Geo-localizationxisting approaches predict the location of a query image by matching it to a database of geo-referenced photographs. While there are many geo-tagged images available on photo sharing and Street View sites, most are clustered around landmarks and urban areas. The vast majority of the Earth’s land are

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Ultrawide Baseline Facade Matching for Geo-localizationbutions or local descriptors fail to match forcing us to rely on the structure of self-similarity of patterns on facades. We propose to capture this structure with a novel “scale-selective self-similarity” (.) descriptor which is computed at each point on the facade at its inherent scale. To achieve

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Recognizing Landmarks in Large-Scale Social Image Collectionszing them effectively. One particularly intuitive way of browsing and searching images is by the geo-spatial location of where on Earth they were taken, but most online images do not have GPS metadata associated with them. We consider the problem of recognizing popular landmarks in large-scale datas

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Worldwide Pose Estimation Using 3D Point Clouds geo-registered 3D point cloud, bringing together research on image localization, landmark recognition, and 3D pose estimation. Our method scales to datasets with hundreds of thousands of images and tens of millions of 3D points through the use of two new techniques: a co-occurrence prior for RANSAC

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Exploiting Spatial and Co-visibility Relations for Image-Based Localizationof the scene. Recent advances in Structure-from-Motion, which allow us to reconstruct large scenes in little time, create a need for image-based localization approaches that handle large-scale models consisting of millions of 3D points both efficiently and effectively in order to localize as many qu

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