An automatic region growing based approach to extract facade textures from single ground-level building images

Öz

An approach is presented for the automatic retrieval of building facade textures from single ground-level building images. The texture information is extracted using the Watershed segmentation which is carried out repetitively until the most successful segment is obtained. To initiate segmentation, the marker pixels are seeded automatically both for foreground facade and background sky, pavement and neighboring buildings regions. The proposed concept was tested on two different datasets. The first dataset contains fifteen rectilinear buildings selected from the residential area of the Batikent district of Ankara, Turkey. The second dataset includes five buildings selected from the eTRIMS database, which contains over one hundred buildings captured in major European cities. The assessment of the segmented facade images was carried out using a quantitative evaluation metric. For both datasets, a quantitative accuracy of above 80% was achieved for facade texture extraction in average. The experimental results indicate that the proposed approach for the automatic retrieval of the facade textures is quite promising and a considerable progress has been made towards the automated construction of the virtual cities.

Anahtar Kelimeler

• Image processing, Facade texture extraction, Watershed segmentation, Accuracy assessment

Cephe dokularının tekli yersel bina görüntülerinden bölge büyütme tabanlı bir yaklaşım kullanılarak otomatik çıkarımı

Öz

Bu çalışmada bina cephe dokularının tekli yersel bina görüntülerinden elde edilmesini sağlayan otomatik bir yaklaşım sunulmaktadır. Doku bilgisi Watershed bölütlemesi kullanılarak çıkarılmakta olup bu işlem en başarılı bölütü elde edene kadar tekrarlı olarak gerçekleştirilmektedir. Bölütlemeyi başlatmak için işaretçi pikseller görüntünün hem ön planına bina cephesi hem de arka planına gökyüzü, kaldırım, komşu binalar otomatik olarak yerleştirilir. Geliştirilen kavram iki farklı veri kümesinde test edilmiştir. Birinci veri kümesi Ankara’nın Batıkent bölgesine ait bir yerleşim yerinden seçilen 15 dörtgensel binayı içermektedir. İkinci veri kümesi ise eTRIMS görüntü veritabanından seçilen 5 binadan oluşmakta olup bu veritabanı Avrupa’nın başlıca kentlerinden çekilmiş yüzün üzerinde binayı içermektedir. Bölütlenen cephe dokularının başarım değerlendirmesi kantitatif bir ölçüm metriği ile gerçekleştirilmiştir. Her iki veri kümesi için de cephe dokusu çıkarımı ortalama %80’in üzerinde bir kantitatif doğrulukla elde edilmiştir. Deneysel sonuçlar bina cephe dokularının tespiti için önerilen bu yaklaşımın umut verici olduğuna ve sanal şehirlerin otomatik üretimine doğru giden yolda önemli bir gelişme kaydedilmekte olduğunu göstermektedir.

Anahtar Kelimeler

• Görüntü işleme, Cephe dokusu çıkarımı, Watershed bölütleme, Başarım değerlendirme

Kaynakça

1. Beucher S., Meyer F., (1992), The Morphological Approach of Segmentation: The Watershed Transformation, Mathematical Morphology in Image Processing' in İçinde, (Dougherty E., Ed.), Marcel Dekker Inc., ss.433-481.
2. Böhm J., (2008), Facade Detail from Incomplete Range Data, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXVII, Part B5, Beijing, Çin.
3. Carlberg M., Andrews J., Gao P., Zakhor A., (2008), Fast Surface Reconstruction and Segmentation with Ground-Based and Airborne LIDAR Range Data, Proceedings of 4th International Symposium on 3D Data Processing, Visualization and Transmission (3DPVT’08), Atlanta, Georgia, ABD.
4. David P., (2008), Detection of Building Facades in Urban Environments, Proceedings of SPIE' nin İçinde, 6978, ss.139– 148, doi: 10.1117/12.779280.
5. Delmerico J. A., David P., Corso J. J., (2011), Building Facade Detection, Segmentation, and Parameter Estimation for Mobile Robot Localization and Guidance, IEEE/RSJ International Conference on Intelligent Robots and Systems' ın İçinde, San Francisco, ABD, ss.1632–1639.
6. Hoegner L., Stilla U., (2009), Thermal Leakage Detection on Building Facades Using Infrared Textures Generated by Mobile Mapping, Proceedings of 2009 Urban Remote Sensing Joint Event, Shangai, Çin.
7. Jang K.H., Jung S.K., (2009), Practical Modeling Technique for Large-scale 3-D Building Models from Ground Images, Pattern Recognition Letters, 30, 861–869, doi: 10.1016/j. patrec.2009.04.004.
8. Kang Z., Zhang L., Zlatanova S., Li J., (2010), An Automatic Mosaicking Method for Building Facade Texture Mapping Using a Monocular Close-range Image Sequences, ISPRS Journal of Photogrammetry and Remote Sensing, 65(3), 282– 293, doi: 10.1016/j.isprsjprs.2009.11.003.

9. Korč F., Förstner W., (2009), eTRIMS Image Database for Interpreting Images of Man-Made Scenes, Technical report TR-IGG-P-2009-01, University of Bonn, Deptartment of Photogrammetry, Almanya.
10. Laycock R.G., Ryder G.D.G., Day A.M., (2007), Automatic Generation, Texturing and Population of a Reflective Real- Time Urban Environment, Computers and Graphics, 31, 625– 635, doi: 10.1016/j.cag.2007.04.001.
11. Lorenz H., Döllner J., (2006), Towards Automating the Generation of Facade Textures of Virtual City Models, ISPRS Commission II, WG II/5 Workshop, Vienna, Avusturya.
12. Martinez J., Soria-Medina A., Arias P., Buffara-Antunes A. F., (2012), Automatic processing of terrestrial laser scanning data of building facades, Automation in Construction, 22, 298–305, doi: 10.1016/j.autcon.2011.09.005
13. Mohan S., Murali S., (2007), Automated 3D Modeling and Rendering from Single View Images, International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007), Tamil Nadu, Hindidtan, doi: 10.1109/ ICCIMA.2007.56.
14. Müller P., Zeng G., Wonka P., Van Gool L., (2007), Image-based Procedural Modeling of Facades, Proceedings of SIGGRAPGH 2007, San Diego, California, ABD.
15. Poullis C., You S., (2009), Photorealistic Large-Scale Urban City Model Reconstruction, IEEE Transactions on Visualization and Computer Graphics, 15(4), 654–669, doi: 10.1109/ TVCG.2008.189.
16. Pu S., Vosselman G., (2009), Knowledge-based Reconstruction of Building Models from Terrestrial Laser Scanning Data, ISPRS Journal of Photogrammetry and Remote Sensing, 64(6), 575– 584, doi: 10.1016/j.isprsjprs.2009.04.001.
17. Rau J. Y., Chu C. Y., (2011), Vector-based Occlusion Detection for Automatic Facade Texture Mapping, In IEEE International Workshop Sensing and Mapping, Xiamen, Çin, ss.1-6, doi: 10.1109/ M2RSM.2011.5697380. Remote
18. Ripperda N., (2008), Determination of Facade Attributes for Facade Reconstruction, Proceedings of International Society for Photogrammetry and Remote Sensing (ISPRS’08) Congress, Beijing, Çin.
19. Shan J., Lee S.D., (2005), Quality of Building Extraction from IKONOS Imagery, Journal of Surveying Engineering, 131(1), 27-32, doi: 10.1061/(ASCE)0733-9453(2005)131:1(27).
20. Shufelt J.A., (1999), Performance Evaluation and Analysis of Monocular Building Extraction from Aerial Imagery, IEEE Transactions on Pattern Analysis and Machine Intelligence, 21(4), 311-326, doi: 10.1109/34.761262.
21. Tian Y., Gerke M., Vosselman G., Zhu Q., (2010), Knowledge-based Building Reconstruction from Terrestrial Video Sequences, ISPRS Journal of Photogrammetry and Remote Sensing, 65(4), 395–408, doi: 10.1016/j.isprsjprs.2010.05.001.
22. Vincent L., Soille P., (1991), Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations, IEEE Transactions on Pattern Analysis and Machine Intelligence, 13(6), 583–598, doi: 10.1109/34.87344.
23. Wan G., Li S., (2011), Automatic Facades Segmentation using Detected Lines and Vanishing Points, 4th International Congress on Image and Signal Processing (CISP). Shanghai, Çin, ss. 1214-1217, doi: 10.1109/CISP.2011.6100448.
24. Wang M., Bai H., Hu F., (2008), Automatic Texture Acquisition for 3D Model Using Oblique Aerial Images. First International Conference on Intelligent Networks and Intelligent Systems (ICINIS 2008). Wuhan, Çin, doi: 10.1109/ICINIS.2008.122.