Finding Line Segments with Tabu Search

Concettina GUERRA; Valerio PASCUCCI

Finding Line Segments with Tabu Search

Concettina GUERRA, Valerio PASCUCCI

Full Text Views

0

Cite this

Summary :

The problem of detecting straight lines arises frequently in image processing and computer vision. Here we consider the problem of extracting lines from range images and more generally from sets of three-dimensional (3D) points. The problem is stated as follows. Given a set Γ of points in 3D space and a non-negative constant , determine the line that is at a distance at most ε from the maximal number of points of . The extraction of multiple lines is achieved iteratively by performing this best line detection and removing at each iteration the points that are close to the line found. We consider two approaches to solve the problem. The first is a simple approach that selects the best line among a randomly chosen subset of lines each defined by a pair of edge points. The second approach, based on tabu search, explores a larger set of candidate lines thus obtaining a better fit of the lines to the points. We present experimental results on different types of three-dimensional data (i) range images of polyhedral objects (ii) secondary structures (helices and strands) of large molecules.

Publication: IEICE TRANSACTIONS on Information Vol.E84-D No.12 pp.1739-1744

Publication Date: 2001/12/01

Publicized

Online ISSN

DOI

Type of Manuscript: Special Section LETTER (Special Issue on Machine Vision Applications)

Category

Cite this

Copy

Concettina GUERRA, Valerio PASCUCCI, "Finding Line Segments with Tabu Search" in IEICE TRANSACTIONS on Information, vol. E84-D, no. 12, pp. 1739-1744, December 2001, doi: .
Abstract: The problem of detecting straight lines arises frequently in image processing and computer vision. Here we consider the problem of extracting lines from range images and more generally from sets of three-dimensional (3D) points. The problem is stated as follows. Given a set Γ of points in 3D space and a non-negative constant , determine the line that is at a distance at most ε from the maximal number of points of . The extraction of multiple lines is achieved iteratively by performing this best line detection and removing at each iteration the points that are close to the line found. We consider two approaches to solve the problem. The first is a simple approach that selects the best line among a randomly chosen subset of lines each defined by a pair of edge points. The second approach, based on tabu search, explores a larger set of candidate lines thus obtaining a better fit of the lines to the points. We present experimental results on different types of three-dimensional data (i) range images of polyhedral objects (ii) secondary structures (helices and strands) of large molecules.
URL: https://global.ieice.org/en_transactions/information/10.1587/e84-d_12_1739/_p

Copy

@ARTICLE{e84-d_12_1739,
author={Concettina GUERRA, Valerio PASCUCCI, },
journal={IEICE TRANSACTIONS on Information},
title={Finding Line Segments with Tabu Search},
year={2001},
volume={E84-D},
number={12},
pages={1739-1744},
abstract={The problem of detecting straight lines arises frequently in image processing and computer vision. Here we consider the problem of extracting lines from range images and more generally from sets of three-dimensional (3D) points. The problem is stated as follows. Given a set Γ of points in 3D space and a non-negative constant , determine the line that is at a distance at most ε from the maximal number of points of . The extraction of multiple lines is achieved iteratively by performing this best line detection and removing at each iteration the points that are close to the line found. We consider two approaches to solve the problem. The first is a simple approach that selects the best line among a randomly chosen subset of lines each defined by a pair of edge points. The second approach, based on tabu search, explores a larger set of candidate lines thus obtaining a better fit of the lines to the points. We present experimental results on different types of three-dimensional data (i) range images of polyhedral objects (ii) secondary structures (helices and strands) of large molecules.},
keywords={},
doi={},
ISSN={},
month={December},}

Copy

TY - JOUR
TI - Finding Line Segments with Tabu Search
T2 - IEICE TRANSACTIONS on Information
SP - 1739
EP - 1744
AU - Concettina GUERRA
AU - Valerio PASCUCCI
PY - 2001
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E84-D
IS - 12
JA - IEICE TRANSACTIONS on Information
Y1 - December 2001
AB - The problem of detecting straight lines arises frequently in image processing and computer vision. Here we consider the problem of extracting lines from range images and more generally from sets of three-dimensional (3D) points. The problem is stated as follows. Given a set Γ of points in 3D space and a non-negative constant , determine the line that is at a distance at most ε from the maximal number of points of . The extraction of multiple lines is achieved iteratively by performing this best line detection and removing at each iteration the points that are close to the line found. We consider two approaches to solve the problem. The first is a simple approach that selects the best line among a randomly chosen subset of lines each defined by a pair of edge points. The second approach, based on tabu search, explores a larger set of candidate lines thus obtaining a better fit of the lines to the points. We present experimental results on different types of three-dimensional data (i) range images of polyhedral objects (ii) secondary structures (helices and strands) of large molecules.
ER -