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
Affine Invariant Texture Segmentation and Shape from Texture by Variational Methods
Journal of Mathematical Imaging and Vision
Computer Vision: A Modern Approach
Computer Vision: A Modern Approach
IMPSAC: Synthesis of Importance Sampling and Random Sample Consensus
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
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
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Image segmentation method using thresholds automatically determined from picture contents
Journal on Image and Video Processing
ASIFT: A New Framework for Fully Affine Invariant Image Comparison
SIAM Journal on Imaging Sciences
PCA-SIFT: a more distinctive representation for local image descriptors
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
A Self-adaptive ASIFT-SH method
Advanced Engineering Informatics
A videogrammetric as-built data collection method for digital fabrication of sheet metal roof panels
Advanced Engineering Informatics
| Hi-index | 0.00 |
When monitoring events on a building site using a system of multiple cameras, it is necessary to establish correspondences between the acquired imaging material. The basic problem when attempting this task is the establishment of any the correspondence between points located on uniform areas of the images (e.g. regions with uniform colour or texture). This paper presents a new robust approach for establishing any correspondence between arbitrarily selected points in two widely-baselined views, based on the ASIFT (affine scale-invariant feature transform) method, image segmentation, and local homography. This method, denoted as ASIFT-SH, consists of the following steps: (i) determination of initial corresponding points, (ii) grouping of initial corresponding points into subsets, (iii) calculation of local homographies for a particular subset of points, and (iv) determination of any correspondence between arbitrary points from a particular camera's view by using a suitable local homography. The ASIFT-SH method, when compared to searching the area surrounding an epipolar line (EPI method), provides more accurate results, especially on surfaces with similar pixel intensities. The average error in our method it is in the order of a few pixels, whilst for the EPI method in the order of a few hundred pixels.