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A Statistical Framework for Long-Range Feature Matching in Uncalibrated Image Mosaicing

Tat-Jen Cham, Roberto Cipolla
CVPR 1998 pp. 442-447
doi:10.1109/CVPR.1998.698643 /cvpr/1998/cham1998cvpr-statistical/

Abstract

The problem considered is that of estimating the projective transformation between two images in situations where the image motion is large and feature matching is not aided by a proximity heuristic. The overall algorithm designed is based on a multiresolution, multihypothesis scheme, and similarities between tracking and matching through multiple resolution levels are exploited. Two major tools are developed in this paper: (i) a Bayesian framework for incorporating similarity measures of feature correspondences in regression to specify the different levels of confidence in the correspondences; and (ii) a Bayesian version of RANSAC, which is able to utilise prior estimates and matching probabilities. The algorithm is tested on a number of real images with large image motion and promising results were obtained.

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Text

Cham and Cipolla. "A Statistical Framework for Long-Range Feature Matching in Uncalibrated Image Mosaicing." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1998. doi:10.1109/CVPR.1998.698643

Markdown

[Cham and Cipolla. "A Statistical Framework for Long-Range Feature Matching in Uncalibrated Image Mosaicing." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1998.](https://mlanthology.org/cvpr/1998/cham1998cvpr-statistical/) doi:10.1109/CVPR.1998.698643

BibTeX

@inproceedings{cham1998cvpr-statistical,
  title     = {{A Statistical Framework for Long-Range Feature Matching in Uncalibrated Image Mosaicing}},
  author    = {Cham, Tat-Jen and Cipolla, Roberto},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {1998},
  pages     = {442-447},
  doi       = {10.1109/CVPR.1998.698643},
  url       = {https://mlanthology.org/cvpr/1998/cham1998cvpr-statistical/}
}