Robust Regression for Data with Multiple Structures

DMCA

Robust Regression for Data with Multiple Structures (2001)

Cached

Download Links

Other Repositories/Bibliography

DBLP

by Haifeng Chen , Peter Meer , David E. Tyler

Venue:	In 2001 IEEE Conference on Computer Vision and Pattern Recognition, volume I
Citations:	24 - 3 self

BibTeX

@INPROCEEDINGS{Chen01robustregression,
    author = {Haifeng Chen and Peter Meer and David E. Tyler},
    title = {Robust Regression for Data with Multiple Structures},
    booktitle = {In 2001 IEEE Conference on Computer Vision and Pattern Recognition, volume I},
    year = {2001},
    pages = {1069--1075}
}

OpenURL

Abstract

In many vision problems (e.g., stereo, motion) multiple structures can occur in the data, in which case several instances of the same model need to be recovered from a single data set. However, once the measurement noise becomes significantly large relative to the separation between the structures, the robust statistical methods commonly used in the vision community tend to fail. In this paper, we show that all these techniques are special cases of the general class of M-estimators with auxiliary scale, and explain their failure in the presence of noisy multiple structures. To be able to cope with data containing multiple structures the techniques innate to vision (Hough and RANSAC) should be combined with the robust methods customary in statistics. The implications of our analysis are illustrated by introducing a simple procedure for 2D multistructured data problematic for all known current techniques. 1.

Keyphrases

multiple structure robust regression noisy multiple structure model need multistructured data problematic single data set current technique many vision problem auxiliary scale robust statistical method measurement noise robust method special case case several instance general class vision community simple procedure

DMCA