Title :
Straight line fitting in a noisy image
Author_Institution :
Center of Autom. Res., Maryland Univ., College Park, MD, USA
Abstract :
The conventional least-squares 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 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, based on a maximum-likelihood principle
Keywords :
computerised picture processing; least squares approximations; statistical analysis; data points; maximum-likelihood; noisy image; picture processing; random noise; statistical method; straight line fitting; Automation; Background noise; Circuit noise; Educational institutions; Fluctuations; Iterative algorithms; Maximum likelihood detection; Noise generators; Noise level; Probability;
Conference_Titel :
Computer Vision and Pattern Recognition, 1988. Proceedings CVPR '88., Computer Society Conference on
Conference_Location :
Ann Arbor, MI
Print_ISBN :
0-8186-0862-5
DOI :
10.1109/CVPR.1988.196305
