Title
Center-of-mass variation under projective transformation
Abstract
Accurate feature detection and localization is fundamentally important to computer vision, and feature locations act as input to many algorithms including camera calibration, structure recovery, and motion estimation. Unfortunately, feature localizers in common use are typically not projectively invariant even in the idealized case of a continuous image. This results in feature location estimates that contain bias which can influence the higher level algorithms that make use of them. While this behavior has been studied in the case of ellipse centroids and then used in a practical calibration algorithm, those results do not trivially generalize to the center-of-mass of a radially symmetric intensity distribution. This paper introduces the generalized result of feature location bias with respect to perspective distortion and applies it to several specific radially symmetric intensity distributions. The impact on calibration is then evaluated. Finally, an initial study is conducted comparing calibration results obtained using center-of-mass to those obtained with an ellipse detector. Results demonstrate that feature localization error, over a range of increasingly large projective distortions, can be stabilized at less than a tenth of a pixel versus errors that can grow to larger than a pixel in the uncorrected case.
Year
DOI
Venue
2007
10.1016/j.patrec.2007.03.018
Pattern Recognition Letters
Keywords
Field
DocType
feature location estimate,idealized case,camera calibration,practical calibration algorithm,accurate feature detection,projective transformation,geometric transformation invariance,feature localization error,feature location bias,feature location,feature localizers,feature detection,center-of-mass variation,calibration result,center of mass,motion estimation
Computer vision,Pattern recognition,Feature (computer vision),Edge detection,Feature extraction,Geometric transformation,Homography,Camera resectioning,Artificial intelligence,Motion estimation,Ellipse,Mathematics
Journal
Volume
Issue
ISSN
28
15
Pattern Recognition Letters
Citations 
PageRank 
References 
2
0.44
18
Authors
2
Name
Order
Citations
PageRank
R. Matt Steele1866.83
Christopher Jaynes224520.92