Details
| Original language | English |
|---|---|
| Pages (from-to) | 841-848 |
| Number of pages | 8 |
| Journal | International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives |
| Volume | XLI-B3 |
| Publication status | Published - 10 Jun 2016 |
| Event | 23rd International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences Congress, ISPRS 2016 - Prague, Czech Republic Duration: 12 Jul 2016 → 19 Jul 2016 |
Abstract
This paper proposes a novel approach for linear feature detection. The contribution is twofold: A novel model for spatial point processes and a new method for linear feature detection. It describes a linear feature as a string of points, represents all features in an image as a configuration of a spatial point process, and formulates feature detection as finding the optimal configuration of a spatial point process. Further, a prior term is proposed to favor straight linear configurations, and a data term is constructed to superpose the points on linear features. The proposed approach extracts straight linear features in a global framework. The paper reports ongoing work. As demonstrated in preliminary experiments, globally optimal linear features can be detected.
Keywords
- Feature Detection, Global Optimization, Linear Feature, Markov Chain Monte Carlo, Simulated Annealing, Spatial Point Processes
ASJC Scopus subject areas
- Computer Science(all)
- Information Systems
- Social Sciences(all)
- Geography, Planning and Development
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In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, Vol. XLI-B3, 10.06.2016, p. 841-848.
Research output: Contribution to journal › Conference article › Research › peer review
}
TY - JOUR
T1 - Detecting linear features by spatial point processes
AU - Chai, Dengfeng
AU - Schmidt, Alena
AU - Heipke, Christian
PY - 2016/6/10
Y1 - 2016/6/10
N2 - This paper proposes a novel approach for linear feature detection. The contribution is twofold: A novel model for spatial point processes and a new method for linear feature detection. It describes a linear feature as a string of points, represents all features in an image as a configuration of a spatial point process, and formulates feature detection as finding the optimal configuration of a spatial point process. Further, a prior term is proposed to favor straight linear configurations, and a data term is constructed to superpose the points on linear features. The proposed approach extracts straight linear features in a global framework. The paper reports ongoing work. As demonstrated in preliminary experiments, globally optimal linear features can be detected.
AB - This paper proposes a novel approach for linear feature detection. The contribution is twofold: A novel model for spatial point processes and a new method for linear feature detection. It describes a linear feature as a string of points, represents all features in an image as a configuration of a spatial point process, and formulates feature detection as finding the optimal configuration of a spatial point process. Further, a prior term is proposed to favor straight linear configurations, and a data term is constructed to superpose the points on linear features. The proposed approach extracts straight linear features in a global framework. The paper reports ongoing work. As demonstrated in preliminary experiments, globally optimal linear features can be detected.
KW - Feature Detection
KW - Global Optimization
KW - Linear Feature
KW - Markov Chain Monte Carlo
KW - Simulated Annealing
KW - Spatial Point Processes
UR - http://www.scopus.com/inward/record.url?scp=84978085274&partnerID=8YFLogxK
U2 - 10.5194/isprs-archives-XLI-B3-841-2016
DO - 10.5194/isprs-archives-XLI-B3-841-2016
M3 - Conference article
AN - SCOPUS:84978085274
VL - XLI-B3
SP - 841
EP - 848
JO - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
JF - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
SN - 1682-1750
T2 - 23rd International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences Congress, ISPRS 2016
Y2 - 12 July 2016 through 19 July 2016
ER -