Skyline matching: A robust registration method between Video and GIS

Zhu, Shupeng; Pressigout, Muriel; Servières, Myriam; Morin, Luce; Moreau, Guillaume

doi:10.1051/3u3d/201203007

Cited by 6 publications

(11 citation statements)

References 9 publications

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“…In the work presented in [2], the basic idea was to compare the partial skyline feature from the image with a synthetic panoramic skyline generated from GIS data using the same camera location, the best match between them providing the camera orientation. However, this method assumes an up-right camera with strict horizontal viewing direction (or optical axis), which means zero pitch and roll.…”

Section: Related Workmentioning

confidence: 99%

“…All the related processes of this system have been discussed in [2] except the vanishing point detection and skyline rectification. So, here we will focus on those two parts.…”

Section: Skyline Rectificationmentioning

confidence: 99%

“…The core of skyline matching algorithm consists of using the CrossSimilarity peak between two skylines [2] and then to estimate the yaw angle. If more than one similar part is present in the panoramic skyline, matching might fail.…”

Section: Temporal Processingmentioning

confidence: 99%

“…The framework of the system will be presented in section 3. This system is an extension of the skyline matching method presented in [2] which can recover the yaw angle from the image with the constraint that all cameras have zero pitch and roll. In order to overcome these limitations, we have developed a method which can calculate pitch and roll angle using vertical vanishing point detection that will be presented in section 4.…”

Section: Introductionmentioning

confidence: 99%

“…Skyline matching: Cross-Similarity result (curve C) between Partial Skyline A and Panoramic Skyline B[2] …”

mentioning

confidence: 99%

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Video/GIS registration system based on skyline matching method

Zhu

Morin

Pressigout

et al. 2013

2013 IEEE International Conference on Image Processing

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International audienceVideo/GIS registration in urban environments is necessary for establishing correspondences between real objects and geometric information in Geographic Information System (GIS) applications such as outdoor Augmented Reality (AR) and building texture acquisition. In this work, we present a registration system using a GPS prior and a skyline matching method to estimate the camera pose from images. A skyline rectification method through vertical vanishing point detection is also developed to enable arbitrary camera orientation, with non nul tilt. The proposed approach is robust, fully automatic and computationally inexpensive which makes it a possible solution for mobile device applications. The performance of our approach is demonstrated in several experimental evaluations. The possible refinement of camera location through a systematic method is also investigated

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Section: Related Workmentioning

confidence: 99%

“…All the related processes of this system have been discussed in [2] except the vanishing point detection and skyline rectification. So, here we will focus on those two parts.…”

Section: Skyline Rectificationmentioning

confidence: 99%

Section: Temporal Processingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Skyline matching: Cross-Similarity result (curve C) between Partial Skyline A and Panoramic Skyline B[2] …”

mentioning

confidence: 99%

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Video/GIS registration system based on skyline matching method

Zhu

Morin

Pressigout

et al. 2013

2013 IEEE International Conference on Image Processing

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The Skyline as a Marker for Augmented Reality in Urban Context

Ayadi

Valque

Scuturici

et al. 2018

Advances in Visual Computing

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In recent years, augmented reality (AR) technologies have emerged as powerful tools to help visualize the future impacts of new constructions on cities. Many approaches that use costly sensors and height-end platforms to run AR in real-time have been developed. Little efforts have been made to embed AR on mobile phones. In this paper, we present a novel approach that uses the Skyline as a marker in an AR system. This lightweight feature enables real-time matching of virtual and real skyline on smartphones. We use device's embedded instruments to estimate the user's pose. This approximation is used to insert a synthetic object in the live video stream. This first approach gives a very unrealistic impression of the viewed scene: the inserted objects appear to hover and float with the user's movements. In order to address this problem, we use the live video camera feed as additional source of information which provides a redundancy to the instruments estimation. We extract the Skyline (a set of pixels that defines the boundary between the building and the sky) as main visual feature. Our proposal is to use these automatically extracted points and track them throughout the video sequence, to allow synthetic objects to anchor these visual features, making it possible to simulate a a landscape from multiple viewpoints using a smartphone. We use images of the Lyon city (France) to illustrate our proposal.

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A New Method of Improving the Azimuth in Mountainous Terrain by Skyline Matching

Nagy

2020

PFG

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Augmented reality (AR) applications have a serious problem with the accuracy of the azimuth angle provided by mobile devices. The fusion of the digital magnetic compass (DMC), accelerometer and gyroscope gives the translation and rotation of the observer in 3D space. However, the precision is not always appropriate since DMC is prone to interference when using it near metal objects or electric currents. The silhouette of ridges separates the sky from the terrain and forms the skyline or horizon line in a mountainous scenery. This salient feature can be used for orientation. With the camera of the device and a digital elevation model (DEM) the correct azimuth angle could be determined. This study proposes an effective method to adjust the azimuth by identifying the skyline from an image and matches it with the skyline of the DEM. This approach does not require manual interaction. The algorithm has also been validated in a real-world environment. ZusammenfassungEine neue Methode zur Verbesserung des Azimuts im Bergland auf Basis des Matchings der Horizontlinie. Augmented Reality (AR)-Anwendungen haben oft ein Problem mit der begrenzten Genauigkeit des Azimuts, das von mobilen Geräten geliefert wird. Die Verknüpfung eines digitalen Magnetkompasses, eines Beschleunigungsmessers und eines Vermessungskreisels liefern die Translation und Rotation eines Beobachters im 3D-Raum. Die Genauigkeit ist jedoch nicht immer brauchbar, weil ein Magnetkompass anfällig für Störungen ist, vor allem verursacht durch Metall und elektrische Ströme. Die Silhouette von Bergen bildet die Horizontlinie, die für eine Richtungsbestimmung (Orientierung) genutzt werden kann. Mit einer Kamera und einem digitalen Höhenmodell (DEM) konnte mit guter Genauigkeit das Azimut bestimmt werden. Die Untersuchung schlägt eine effektive Methode vor, um das Azimut eines Magnetkompasses durch Vergleich der Horizontlinie aus einem Bild mit der gerechneten Horizontlinie aus einem DEM zu bestimmen. Dieser Ansatz benötigt keinen manuellen Eingriff. Der Algorithmus wurde in realen Umgebungen validiert.

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Skyline matching: A robust registration method between Video and GIS

Cited by 6 publications

References 9 publications

Video/GIS registration system based on skyline matching method

Video/GIS registration system based on skyline matching method

The Skyline as a Marker for Augmented Reality in Urban Context

A New Method of Improving the Azimuth in Mountainous Terrain by Skyline Matching

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