Computer Vision, Graphics, and Image Processing
Geometric computation for machine vision
Geometric computation for machine vision
Creating full view panoramic image mosaics and environment maps
Proceedings of the 24th annual conference on Computer graphics and interactive techniques
Geometric Information Criterion for Model Selection
International Journal of Computer Vision
Statistical Optimization for Geometric Computation: Theory and Practice
Statistical Optimization for Geometric Computation: Theory and Practice
Robust Video Mosaicing through Topology Inference and Local to Global Alignment
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Efficient 3-D Scene Visualization by Image Extrapolation
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Model Selection for Two View Geometry: A Review
Shape, Contour and Grouping in Computer Vision
Using geometric corners to build a 2D mosaic from a set of image
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
A Unified Factorization Algorithm for Points, Line Segments and Planes with Uncertainty Models
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Comparing Intensity Transformations and Their Invariants in the Context of Color Pattern Recognition
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Uncertainty Modeling and Model Selection for Geometric Inference
IEEE Transactions on Pattern Analysis and Machine Intelligence
The geometric error for homographies
Computer Vision and Image Understanding
| Hi-index | 0.00 |
The computation for image mosaicing using homographies is numerically unstable and causes large image distortions if the matching points are small in number and concentrated in a small region in each image. This instability stems from the fact that actual transformations of images are usually in a small subgroup of the group of homographies. It is shown that such undesirable distortions can be removed by model selection using the geometric AIC without introducing any empirical thresholds. It is shown that the accuracy of image mosaicing can be improved beyond the theoretical bound imposed on statistical optimization. This is made possible by our knowledge about probable subgroups of the group of homographies. We demonstrate the effectiveness of our method by real image examples.