Buildings Recognition and Camera Localization Using Image Texture Description
•Als PPT, PDF herunterladen•
0 gefällt mir•687 views
3D GIS model/2D image registration called much attention in the recent years because of its vast range of potential applications in real and virtual navigation. However, automatic registration remains until now a challenge. This paper presents a methodology for enhancing and complementing a GIS database of buildings with a descriptor of their texture by using information extracted from a reference images. This descriptor is used to locate any other image by searching similar texture in the image. Then the absolute position and orientation of the camera of the new image can be computed if the camera parameters (like focal length) are known. The paper proposes a technique that can be used for achieving the identification of the facade in the photograph, calibrated camera geolocation and discusses the quality of the results.
![Buildings Recognition and Camera Localization Using Image Texture Description SULEIMAN Wassim 1 , JOLIVEAU Thierry 1 , FAVIER Eric 2 1 ISTHME-ISIG CNRS/UMR EVS, Université Jean Monnet - Saint-Etienne. 2 DIPI EA 3719 École Nationale d'Ingénieurs de Saint-Etienne [email_address] [email_address] [email_address] 25th International Cartographic Conference (Sageo) – 8 july 2011 – Palais de congrès Paris](https://image.slidesharecdn.com/sageoanglais11-110909155109-phpapp01/85/Buildings-Recognition-and-Camera-Localization-Using-Image-Texture-Description-1-320.jpg)
Empfohlen
Empfohlen
Weitere ähnliche Inhalte
Was ist angesagt?
Was ist angesagt? (19)
Ähnlich wie Buildings Recognition and Camera Localization Using Image Texture Description
Ähnlich wie Buildings Recognition and Camera Localization Using Image Texture Description (20)
Kürzlich hochgeladen
Kürzlich hochgeladen (20)
Buildings Recognition and Camera Localization Using Image Texture Description
- 1. Buildings Recognition and Camera Localization Using Image Texture Description SULEIMAN Wassim 1 , JOLIVEAU Thierry 1 , FAVIER Eric 2 1 ISTHME-ISIG CNRS/UMR EVS, Université Jean Monnet - Saint-Etienne. 2 DIPI EA 3719 École Nationale d'Ingénieurs de Saint-Etienne [email_address] [email_address] [email_address] 25th International Cartographic Conference (Sageo) – 8 july 2011 – Palais de congrès Paris
- 2. Objective Find a building in an image SIG 3D 3D GIS Locate the camera that took the image by using the location of the building
- 7. Enhancing GIS databases with building texture information Texture analyses (SIFT) Finding the interest points with their local descriptor
- 8. Enhancing GIS databases with building texture information Finding the (x,y,z) of the interest points Homography constraints 3D GIS model
- 9. Enhancing GIS databases with building texture information The texture descriptor : list of interest points with their local descriptor and their 3D position
- 11. Facade recognition False matching because of the locality of the descriptor
- 13. Facade recognition The facade in the 3D GIS
- 15. Camera Geolocation Association of the interest points with the 3D position of the matched points in the GIS databases
- 16. Camera geolocation 4 points non-collinear (Yang & al. 2009) Real position Measured position
- 28. Thank you For your attention Suleiman wassim [email_address]
Hinweis der Redaktion
- The results are not always good, because of the error in the 3D model, the complexity of urban environment and the collusion Sourimant et al., 2009. GPS, GIS AND VIDEO REGISTRATION FOR BUILDING RECONSTRUCTION
- The results are not always good, because of the error in the 3D model, the complexity of urban environment and the collusion Sourimant et al., 2009. GPS, GIS AND VIDEO REGISTRATION FOR BUILDING RECONSTRUCTION
- The results are not always good, because of the error in the 3D model, the complexity of urban environment and the collusion Sourimant et al., 2009. GPS, GIS AND VIDEO REGISTRATION FOR BUILDING RECONSTRUCTION
- SURF is faster than SIFT descriptor. But our tests show that the SIFT descriptor gives better results when the angle of view vary between two images SIFT code of VLFEAT and SURF code from OPENSURF. http://www.vlfeat.org/~vedaldi/code/sift.html http://www.mathworks.com/matlabcentral/fileexchange/28300
- SURF is faster than SIFT descriptor. But our tests show that the SIFT descriptor gives better results when the angle of view vary between two images SIFT code of VLFEAT and SURF code from OPENSURF. http://www.vlfeat.org/~vedaldi/code/sift.html http://www.mathworks.com/matlabcentral/fileexchange/28300
- SURF is faster than SIFT descriptor. But our tests show that the SIFT descriptor gives better results when the angle of view vary between two images SIFT code of VLFEAT and SURF code from OPENSURF. http://www.vlfeat.org/~vedaldi/code/sift.html http://www.mathworks.com/matlabcentral/fileexchange/28300
- The results are not always good, because of the error in the 3D model, the complexity of urban environment and the collusion Sourimant et al., 2009. GPS, GIS AND VIDEO REGISTRATION FOR BUILDING RECONSTRUCTION
- The level of the recognition based on the number of the interest points and their discriminative descriptor
- The level of the recognition based on the number of the interest points and their discriminative descriptor
- The level of the recognition based on the number of the interest points and their discriminative descriptor
- The results are not always good, because of the error in the 3D model, the complexity of urban environment and the collusion Sourimant et al., 2009. GPS, GIS AND VIDEO REGISTRATION FOR BUILDING RECONSTRUCTION
- With four points (three non collinear scene points or non collinear image points). (Yang et al. 2009) is applied. It is based on the homography matrix between the front wall in the query image and the 3D model. The best result is obtained when the camera is located just in front of the building at a distance less than 100 meters. The quality falls down steeply also if the distance between the camera and the building exceeds 70 m (low resolution), and the view angle is above 30 degrees (non affine descriptor).
- With four points (three non collinear scene points or non collinear image points). (Yang et al. 2009) is applied. It is based on the homography matrix between the front wall in the query image and the 3D model. The best result is obtained when the camera is located just in front of the building at a distance less than 100 meters. The quality falls down steeply also if the distance between the camera and the building exceeds 70 m (low resolution), and the view angle is above 30 degrees (non affine descriptor).
- We need at least 4 non coplanar points. The SOFTPOSIT algorithm (David et al. 2004) is used which has a high efficiency If one of the discovered facades shows an angle above 40 degrees, this facade will cause more error in the computation and contribute to the drop down of accuracy in the position of the camera
- The results are not always good, because of the error in the 3D model, the complexity of urban environment and the collusion Sourimant et al., 2009. GPS, GIS AND VIDEO REGISTRATION FOR BUILDING RECONSTRUCTION
- Approximate position and orientation of the camera are the first step of an automatic registration process Urban environment with low GPS precision (Li et al. 2006) (Bioret et al. 2009). Affine descriptor (Mikolajczyk & Schmid 2004) for the facade texture. This could greatly ameliorate the result for multiple facades detection If the method should be applied on Smartphones with continue video data, the SURF descriptor could perform better than SIFT because it is faster ( http://www.kooaba.com/ )
- Approximate position and orientation of the camera are the first step of an automatic registration process Urban environment with low GPS precision (Li et al. 2006) (Bioret et al. 2009). Affine descriptor (Mikolajczyk & Schmid 2004) for the facade texture. This could greatly ameliorate the result for multiple facades detection If the method should be applied on Smartphones with continue video data, the SURF descriptor could perform better than SIFT because it is faster ( http://www.kooaba.com/ )
- Approximate position and orientation of the camera are the first step of an automatic registration process Urban environment with low GPS precision (Li et al. 2006) (Bioret et al. 2009). Affine descriptor (Mikolajczyk & Schmid 2004) for the facade texture. This could greatly ameliorate the result for multiple facades detection If the method should be applied on Smartphones with continue video data, the SURF descriptor could perform better than SIFT because it is faster ( http://www.kooaba.com/ )
- The results are not always good, because of the error in the 3D model, the complexity of urban environment and the collusion Sourimant et al., 2009. GPS, GIS AND VIDEO REGISTRATION FOR BUILDING RECONSTRUCTION