A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Artificial Intelligence - Special volume on computer vision
Automatic object extraction from aerial imagery—a survey focusing on buildings
Computer Vision and Image Understanding
Multiple view geometry in computer vision
Multiple view geometry in computer vision
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Non-parametric Model for Background Subtraction
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Recognition and reconstruction of buildings from multiple aerial images
Computer Vision and Image Understanding
Improved Rooftop Detection in Aerial Images with Machine Learning
Machine Learning
An Efficient Solution to the Five-Point Relative Pose Problem
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
3D Building Detection and Modeling from Aerial LIDAR Data
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Integrating LiDAR, Aerial Image and Ground Images for Complete Urban Building Modeling
3DPVT '06 Proceedings of the Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06)
Automatic description of complex buildings from multiple images
Computer Vision and Image Understanding
| Hi-index | 0.00 |
This paper presents a prototype system of rooftop detection and 3D building modeling from aerial images. In this system, without the knowledge of the position and orientation information of the aerial vehicle a priori, the parameters of the camera pose and ground plane are first estimated by simple human---computer interaction. Next, after an over-segmentation of the aerial image by the Mean-Shift algorithm, the rooftop regions are coarsely detected by integrating multi-scale SIFT-like feature vectors with SVM-based visual object recognition. 2D cues alone however might not always be sufficient to separate regions such as parking lots from building roofs. Thus in order to further refine the accuracy of the roof-detection result and remove the misclassified non-rooftop regions such as parking lots, we further resort to 3D depth information estimated based on multi-view geometry. More specifically, we determine whether a candidate region is a rooftop or not according to its height information relative to the ground plane, whereas the candidate region's height information is obtained by a novel, hierarchical, asymmetry correlation-based corner matching scheme. The output of the system will be a water-tight triangle mesh based 3D building model texture mapped with the aerial images. We developed an interactive 3D viewer based on OpenGL and C+驴+ to allow the user to virtually navigate the reconstructed 3D scene with mouse and keyboard. Experimental results are shown on real aerial scenes.