2015
DOI: 10.1016/j.imavis.2015.07.003
|View full text |Cite
|
Sign up to set email alerts
|

Application of gradient-based edge detectors to determine vanishing points in monoscopic images: Comparative study

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1

Citation Types

0
2
0

Year Published

2016
2016
2024
2024

Publication Types

Select...
4
2

Relationship

0
6

Authors

Journals

citations
Cited by 6 publications
(2 citation statements)
references
References 29 publications
0
2
0
Order By: Relevance
“…Edge pixels are defined as a set of points (pixels) located on a curve that separates the neighboring points (pixels) or points (pixels) on the other side of the curve, which differ in brightness. The aim of the project was to detect local discontinuities in the brightness level and the outline of the research material [ 45 , 46 , 47 ]. The Kirsch mask consists of matching the perfect pattern to the edge pixels of the research material.…”
Section: Methodsmentioning
confidence: 99%
“…Edge pixels are defined as a set of points (pixels) located on a curve that separates the neighboring points (pixels) or points (pixels) on the other side of the curve, which differ in brightness. The aim of the project was to detect local discontinuities in the brightness level and the outline of the research material [ 45 , 46 , 47 ]. The Kirsch mask consists of matching the perfect pattern to the edge pixels of the research material.…”
Section: Methodsmentioning
confidence: 99%
“…Establishing vanishing points plays an important role not only in perspective creation but also in the reverse process that is the reconstruction of perspective [13,14]. Therefore, several works deal with automatic detection of the vanishing points in monoscopic image, which is the first step to three dimensional data extraction [15][16][17]. Much work has been done in the field of perspective analyses and perspective construction of the architectural environment onto a single flat projection plane [2][3][4][5][6].…”
Section: Introductionmentioning
confidence: 99%