DocumentCode
910202
Title
Line fitting in a noisy image
Author
Weiss, Isaac
Author_Institution
Center for Autom. Res., Maryland Univ., College Park, MD, USA
Volume
11
Issue
3
fYear
1989
fDate
3/1/1989 12:00:00 AM
Firstpage
325
Lastpage
329
Abstract
The conventional least-squared-distance method of fitting a line to a set of data points is unreliable when the amount of random noise in the input (such as an image) is significant compared with the amount of data correlated to the line itself. Points which are far away from the line (outliers) are usually just noise, but they contribute the most to the distance averaging, skewing the line from its correct position. The author presents a statistical method of separating the data of interest from random noise, using a maximum-likelihood principle
Keywords
noise; picture processing; probability; statistical analysis; line fitting; maximum-likelihood principle; noisy image; picture processing; random noise; statistical method; Automation; Circuit noise; Fluctuations; Iterative algorithms; Least squares methods; Maximum likelihood detection; Noise generators; Noise level; Probability; Statistical analysis;
fLanguage
English
Journal_Title
Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publisher
ieee
ISSN
0162-8828
Type
jour
DOI
10.1109/34.21801
Filename
21801
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