Do we really have to consider covariance matrices for image features?
- 13 November 2002
- proceedings article
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 2, 301-306
- https://doi.org/10.1109/iccv.2001.937640
Abstract
Many studies have been made in the past for optimization using covariance matrices of feature points. We first describe how to compute the covariance matrix of a feature point from the gray levels by integrating existing methods. Then, we experimentally examine if thus computed covariance matrices really reflect the accuracy of the feature points. To test this, we do subpixel tem- plate matching and compute the homography and the fundamental matrix. Our conclusion is rather surprising, pointing out impor- tant elements often overlooked.Keywords
This publication has 8 references indexed in Scilit:
- An estimation-theoretic framework for image-flow computationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- A unified factorization algorithm for points, line segments and planes with uncertainty modelsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Optimal filters for gradient-based motion estimationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1999
- SUSAN—A New Approach to Low Level Image ProcessingInternational Journal of Computer Vision, 1997
- Context-free attentional operators: The generalized symmetry transformInternational Journal of Computer Vision, 1995
- Markov Random Field Modeling in Computer VisionPublished by Springer Nature ,1995
- A Combined Corner and Edge DetectorPublished by British Machine Vision Association and Society for Pattern Recognition ,1988
- Reliability analysis of parameter estimation in linear models with applications to mensuration problems in computer visionComputer Vision, Graphics, and Image Processing, 1987