Geometric invariance in computer vision
Geometric invariance in computer vision
Computational cross ratio for computer vision
CVGIP: Image Understanding
Stochastic properties of the cross ratio
Pattern Recognition Letters
Projectively invariant decomposition and recognition of planar shapes
International Journal of Computer Vision - Special issue: machine vision research at the Royal Institute of Technology
Relative Affine Structure: Canonical Model for 3D From 2D Geometry and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Obtaining base edge correspondence in stereo images via quantitative measures along C-diagonals
Pattern Recognition Letters
Error Guided Design of a 3D Vision System
IEEE Transactions on Pattern Analysis and Machine Intelligence
Identity verification by relative 3-D structure using multiple facial images
Pattern Recognition Letters
An invariant representation for matching trajectories across uncalibrated video streams
CIVR'05 Proceedings of the 4th international conference on Image and Video Retrieval
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Recently, more and more computer vision researchers are paying attention to error analysis so as to fulfill various accuracy requirements arising from different applications. As a geometric invariant under projective transformations, cross-ratio is the basis of many recognition and reconstruction algorithms which are based on projective geometry. We propose an efficient way of analyzing localization error for computer vision systems which use cross-ratios in planar localization. By studying the inaccuracy associated with cross-ratio-based computations, we inspect the possibility of using linear transformation to approximate localization error due to 2-D noises of image extraction for reference points. Based on such a computationally efficient analysis, a practical way of choosing point features in an image so as to establish the probabilistically most accurate planar location system using crossratios is developed.