Local Grayvalue Invariants for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Determining the Epipolar Geometry and its Uncertainty: A Review
International Journal of Computer Vision
IMPSAC: Synthesis of Importance Sampling and Random Sample Consensus
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
SVD-matching using SIFT features
Graphical Models - Special issue on the vision, video and graphics conference 2005
Automated construction activity monitoring system
Advanced Engineering Informatics
Advanced Engineering Informatics
ASIFT: A New Framework for Fully Affine Invariant Image Comparison
SIAM Journal on Imaging Sciences
Estimating correspondence between arbitrarily selected points in two widely-separated views
Advanced Engineering Informatics
Location recognition using prioritized feature matching
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
Communications of the ACM
Automated vision tracking of project related entities
Advanced Engineering Informatics
Discrete-continuous optimization for large-scale structure from motion
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Where's Waldo: Matching people in images of crowds
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Image matching using local symmetry features
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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When monitoring events on a building site using a system of multiple cameras, it is necessary to establish correspondence between the acquired imaging material. The basic problem when attempting this task is the establishment of any correspondence between points located on uniform areas of the images (e.g. regions with uniform colour or texture). The basic version of our ASIFT-SH method can mainly solve such a problem. This method consists of four steps: (i) determining the initial corresponding points within the images of both views by using the ASIFT method, (ii) grouping of initial corresponding points from the first step into subsets, based on segmented regions, (iii) calculation of local homographies for a particular subset of corresponding points, and (iv) determining any correspondence between arbitrary points from a particular camera's viewpoint, by using a suitable local homography. The critical step of this method concerns segmentation. Therefore, we have introduced into our algorithm a step for adaptive adjustment, the segmented regions being remodelled so that they better meet the required coplanarity criterion. This introduced step is based on a 3D reconstruction of the initial corresponding points and a search for the minimal number of planes within the 3D space to which these points belong. Those points that belong to a particular plane, represent a newly-created subset of the initial corresponding points. The results point out that the introduction of this adaptive step into ASIFT-SH significantly improves the accuracy of corresponding points' calculation. The mean error is 1.63 times lower and the standard deviation is 2.56 times lower than by the basic version of the ASIFT-SH method.