Title :
Finding line segments by stick growing
Author :
Nelson, Randal C.
Author_Institution :
Dept. of Comput. Sci., Rochester Univ., NY, USA
fDate :
5/1/1994 12:00:00 AM
Abstract :
A method is described for extracting lineal features from an image using extended local information to provide robustness and sensitivity. The method utilizes both gradient magnitude and direction information, and incorporates explicit lineal and end-stop terms. These terms are combined nonlinearly to produce an energy landscape in which local minima correspond to lineal features called sticks that can be represented as line segments. A hill climbing (stick-growing) process is used to find these minima. The method is compared to two others, and found to have improved gap-crossing characteristics
Keywords :
feature extraction; image segmentation; end-stop terms; extended local information; hill climbing; line segment finding; lineal feature extraction; robustness; sensitivity; stick growing; Data mining; Feature extraction; Geometry; Image edge detection; Image recognition; Pattern recognition; Robot sensing systems; Robotics and automation; Robustness; Shape;
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on