This paper introduces \scshapeLocal Lines ––a robust, high-resolution
line detector that operates in linear time. \scshapeLocal Lines
tolerates noisy images well and can be optimized for various specialized
applications by adjusting the values of configurable parameters,
such as mask values and mask size. As described in this paper, the
resolution of Image Image Image Image is the maximum that can be
justified for pixelized data. Despite this high resolution, \scshapeLocal
Lines is of linear asymptotic complexity in terms of number of pixels
in an image. This paper also provides a comparison of \scshapeLocal
Lines with the prevalent Hough Transform Line Detector.
%0 Journal Article
%1 Climer2003
%A Climer, Sharlee
%A Bhatia, Sanjiv K.
%D 2003
%J Pattern Recognition Letters (PATREC)
%K Line Linear Low complexity; detector; extraction feature level
%N 14
%P 2291--2300
%R 10.1016/S0167-8655(03)00055-2
%T Local Lines: A linear time line detector
%U http://www.sciencedirect.com/science/article/B6V15-48M7YK2-1/2/9660aa78714c7b772b421ab1ee72d379
%V 24
%X This paper introduces \scshapeLocal Lines ––a robust, high-resolution
line detector that operates in linear time. \scshapeLocal Lines
tolerates noisy images well and can be optimized for various specialized
applications by adjusting the values of configurable parameters,
such as mask values and mask size. As described in this paper, the
resolution of Image Image Image Image is the maximum that can be
justified for pixelized data. Despite this high resolution, \scshapeLocal
Lines is of linear asymptotic complexity in terms of number of pixels
in an image. This paper also provides a comparison of \scshapeLocal
Lines with the prevalent Hough Transform Line Detector.
@article{Climer2003,
abstract = {This paper introduces {\scshape{}Local Lines} ––a robust, high-resolution
line detector that operates in linear time. {\scshape{}Local Lines}
tolerates noisy images well and can be optimized for various specialized
applications by adjusting the values of configurable parameters,
such as mask values and mask size. As described in this paper, the
resolution of Image Image Image Image is the maximum that can be
justified for pixelized data. Despite this high resolution, {\scshape{}Local
Lines} is of linear asymptotic complexity in terms of number of pixels
in an image. This paper also provides a comparison of {\scshape{}Local
Lines} with the prevalent Hough Transform Line Detector.},
added-at = {2011-03-27T19:47:06.000+0200},
author = {Climer, Sharlee and Bhatia, Sanjiv K.},
biburl = {https://www.bibsonomy.org/bibtex/2c0711fb80a1607440eda6af954e22f0e/cocus},
doi = {10.1016/S0167-8655(03)00055-2},
file = {:./Climer.pdf:PDF},
interhash = {4e8066cca07c94c8a96a9691df7179e7},
intrahash = {c0711fb80a1607440eda6af954e22f0e},
journal = {Pattern Recognition Letters ({PATREC})},
keywords = {Line Linear Low complexity; detector; extraction feature level},
number = 14,
owner = {CK},
pages = {2291--2300},
timestamp = {2011-03-27T19:47:07.000+0200},
title = {Local Lines: A linear time line detector},
url = {http://www.sciencedirect.com/science/article/B6V15-48M7YK2-1/2/9660aa78714c7b772b421ab1ee72d379},
volume = 24,
year = 2003
}