Morphological Segmentation of Building Façade Images

ISBN : 978-1-4244-5653-6International audienceIn this paper, we describe an automatic method for segmentation of building façade images. First, individual façades are isolated from general city block images. This step is based on accumulation of directional color gradients, assuming that façade structures are aligned. Then sky region is detected based on segmentation approach and color marker extraction. Finally, the images are split in floors using directional color gradient accumulation, as well. Our approach introduces several morphological filters to augment the robustness to problems such as: textured balconies, some specular reflections of the bright windows and small obstacles in images. The experimental results show the performance of our approach

Vision-Based Building Seismic Displacement Measurement by Stratification of Projective Rectification Using Lines

We propose a new flexible technique for accurate vision-based seismic displacement measurement of building structures via a single non-stationary camera with any perspective view. No a priori information about the camera’s parameters or only partial knowledge of the internal camera parameters is required, and geometric constraints in the world coordinate system are employed for projective rectification in this research. Whereas most projective rectifications are conducted by specifying the positions of four or more fixed reference points, our method adopts a stratified approach to partially determine the projective transformation from line-based geometric relationships on the world plane. Since line features are natural and plentiful in a man-made architectural building environment, robust estimation techniques for automatic projective/affine distortion removal can be applied in a more practical way. Both simulations and real-recorded data were used to verify the effectiveness and robustness of the proposed method. We hope that the proposed method could advance the consumer-grade camera system for vision-based structural measurement one more step, from laboratory environments to real-world structural health monitoring systems