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Hierarchical Image Geo-Location On a...

Vasile, Alexandru Nicolae.

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Hierarchical Image Geo-Location On a World-Wide Scale.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Hierarchical Image Geo-Location On a World-Wide Scale./
作者:	Vasile, Alexandru Nicolae.
面頁冊數:	113 p.
附註:	Source: Dissertation Abstracts International, Volume: 76-04(E), Section: B.
Contained By:	Dissertation Abstracts International76-04B(E).
標題:	Computer science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3667933
ISBN:	9781321424997

Hierarchical Image Geo-Location On a World-Wide Scale.
Vasile, Alexandru Nicolae.

Hierarchical Image Geo-Location On a World-Wide Scale. - 113 p.

Source: Dissertation Abstracts International, Volume: 76-04(E), Section: B.

Thesis (Ph.D.)--Northeastern University, 2015.

This item is not available from ProQuest Dissertations & Theses.

There are increasingly vast amounts of imagery and video collected from a variety of sensor modalities. Considering that each individual image may contain considerable amounts of information, the ability to interpret, understand and extract scene information is highly beneficial. In order to enable automated scene understanding, there is a need for an organizing principle to store, visualize and exploit the data. Three-dimensional geometry provides such an organizing principle as imagery and video have inherent 3D structure and can be associated with geographic coordinates. In this thesis, we leverage multiple large geo-spatial databases to create a 3D world model and develop a hierarchical image geo-location framework using a coarse-to-fine localization approach. Starting at the coarsest level, a query image is geo-located to regions of the world though a probabilistic terrain classification approach using a 6.5 million image Flickr database. Next, a novel medium-scale localization method is developed to rule out most of the regions and establish candidate geo-locations with geo-positioning accuracy at a city level. Results from the combined hierarchical classifier demonstrate a 10% improvement over current state-of-the-art. A fine-scale geo-location stage was also developed to determine the pose of a query image to street-level geo-positioning accuracy. The fine-scale algorithm introduced an efficient structure-from-motion (SfM) 3D reconstruction approach that scales to city-sized image databases, incorporating ground video imagery as well as aerial video imagery for a more complete 3D city model. The newly developed SfM approach is demonstrated to have an order of magnitude computational speed-up compared to prior work, and validated to produce a 3D city model that is absolutely geo-located to within 3 meters compared to 3D Laser radar (Ladar) truth imagery. The fine geo-location stage was also tested using a 500 image hold-out set and demonstrated to geo-locate close to 80% of query images to within better than 100m, exceeding the system goal of street-level geo-positioning accuracy. As a proof-of-concept, we demonstrate improved image understanding by leveraging the newly developed 3D world model to perform information transference to example query images from other geo-located, labeled data sources.

ISBN: 9781321424997Subjects--Topical Terms:

523869
Computer science.

Hierarchical Image Geo-Location On a World-Wide Scale.
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520 $a There are increasingly vast amounts of imagery and video collected from a variety of sensor modalities. Considering that each individual image may contain considerable amounts of information, the ability to interpret, understand and extract scene information is highly beneficial. In order to enable automated scene understanding, there is a need for an organizing principle to store, visualize and exploit the data. Three-dimensional geometry provides such an organizing principle as imagery and video have inherent 3D structure and can be associated with geographic coordinates. In this thesis, we leverage multiple large geo-spatial databases to create a 3D world model and develop a hierarchical image geo-location framework using a coarse-to-fine localization approach. Starting at the coarsest level, a query image is geo-located to regions of the world though a probabilistic terrain classification approach using a 6.5 million image Flickr database. Next, a novel medium-scale localization method is developed to rule out most of the regions and establish candidate geo-locations with geo-positioning accuracy at a city level. Results from the combined hierarchical classifier demonstrate a 10% improvement over current state-of-the-art. A fine-scale geo-location stage was also developed to determine the pose of a query image to street-level geo-positioning accuracy. The fine-scale algorithm introduced an efficient structure-from-motion (SfM) 3D reconstruction approach that scales to city-sized image databases, incorporating ground video imagery as well as aerial video imagery for a more complete 3D city model. The newly developed SfM approach is demonstrated to have an order of magnitude computational speed-up compared to prior work, and validated to produce a 3D city model that is absolutely geo-located to within 3 meters compared to 3D Laser radar (Ladar) truth imagery. The fine geo-location stage was also tested using a 500 image hold-out set and demonstrated to geo-locate close to 80% of query images to within better than 100m, exceeding the system goal of street-level geo-positioning accuracy. As a proof-of-concept, we demonstrate improved image understanding by leveraging the newly developed 3D world model to perform information transference to example query images from other geo-located, labeled data sources.
520 $a In support of fine-scale geo-location validation, we also developed an algorithm to process 3D Ladar data using a novel 3D noise filtering technique that is shown to be a significant improvement over current state of the art, resulting in a 9x improvement in signal-to-noise ratio, a 2-3x improvement in angular and range resolution, a 21% improvement in ground detection and a 5.9x improvement in computational efficiency.
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