Image-based Structural Analysis of Building using Line Segments and their Geometrical Vanishing Points

This paper describes an approach to detect and analyze the properties of building in image. We use line segments and belongings in the appearance of building as geometrical and physical properties respectively. The geometrical properties are represented as principal component parts (PCPs) as a set of door, window, wall and so on. As the physical properties, color, intensity, contrast and texture of regions are used. Analysis process is started by detecting straight line segments. We use MSAC to group such parallel line segments which have a common vanishing point. We calculate one dominant vanishing point for vertical direction and five dominant vanishing points in maximum for horizontal direction. A mesh of basic parallelograms is created by one of horizontal groups and vertical group. Each mesh represents one face of building. The PCPs are formed by merging neighborhood of basic parallelograms which have similar colors. The wall regions of PCPs are detected. Finally, the structure of building is described as a system of hierarchical features. The building is represented by number of faces. Each face is regarded by a color histogram vector. The color histogram vector just is computed by wall region of face. The proposed approach was used to recognize a database containing 1005 images and 115 queried images. It has been confirmed with various kinds of images taken for different conditions like camera systems, weather and seasons

[1]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[2]  Luc Van Gool,et al.  Fast indexing for image retrieval based on local appearance with re-ranking , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).

[3]  Bernhard P. Wrobel,et al.  Multiple View Geometry in Computer Vision , 2001 .

[4]  Yi Li,et al.  Consistent line clusters for building recognition in CBIR , 2002, Object recognition supported by user interaction for service robots.

[5]  Luc Van Gool,et al.  HPAT Indexing for Fast Object/Scene Recognition Based on Local Appearance , 2003, CIVR.

[6]  George Mason,et al.  Experiments in Building Recognition , 2004 .

[7]  Peter J. Rousseeuw,et al.  Robust Regression and Outlier Detection , 2005, Wiley Series in Probability and Statistics.

[8]  Andrew Zisserman,et al.  MLESAC: A New Robust Estimator with Application to Estimating Image Geometry , 2000, Comput. Vis. Image Underst..

[9]  Wei Zhang,et al.  Localization Based on Building Recognition , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops.

[10]  Roberto Cipolla,et al.  An Image-Based System for Urban Navigation , 2004, BMVC.

[11]  Y. Sakai,et al.  Anomaly Detection for Autonomous Inspection of Space Facilities using Camera Images , 2006, 2006 SICE-ICASE International Joint Conference.

[12]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[13]  Jake K. Aggarwal,et al.  Applying perceptual grouping to content-based image retrieval: building images , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[14]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .