Detecting buildings in aerial images
Computer Vision, Graphics, and Image Processing
3D object recognition using invariance
Artificial Intelligence - Special volume on computer vision
Canonical representations for the geometries of multiple projective views
Computer Vision and Image Understanding
Relative Affine Structure: Canonical Model for 3D From 2D Geometry and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Lines and Points in Three Views and the Trifocal Tensor
International Journal of Computer Vision
The Quadric Reference Surface: Theory and Applications
International Journal of Computer Vision
Computer Vision and Image Understanding
The Cascaded Hough Transform as an Aid in Aerial Image Interpretation
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Quadric Reconstruction from Dual-Space Geometry
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
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This paper proposes an invariance based recognition scheme for scenes with multiple repeated components. The scheme considers three component subsets which characterize the scene completely. Each such three component subset is reconstructed using single image based information. We have developed a mathematical framework for the projective reconstruction based on relative affine structure of each such three component building block. This is extended to the case when each of the components is a quadric. A set of projective invariants of three quadrics has also been obtained by us. Although the reconstruction scheme is general and applicable to all multiple repeated components, it requires the computation of infinite homography. The infinite homography and hence the reconstruction scheme are only image computable with the given information in the case of translational repetition. We therefore develop a recognition strategy for the specific case of translationally repeated quadrics. As a recognition strategy for scenes with multiple translationally repeated quadric components, we propose to compute and store invariant values for each such three component subsets. Experiments on real data have shown the applicability of this approach for recognition of aerial images of power plants. The discriminatory power of the invariants and the stability of the recognition results have also been experimentally demonstrated.