Details
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 841-848 |
| Seitenumfang | 8 |
| Fachzeitschrift | International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives |
| Jahrgang | XLI-B3 |
| Publikationsstatus | Veröffentlicht - 10 Juni 2016 |
| Veranstaltung | 23rd International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences Congress, ISPRS 2016 - Prague, Tschechische Republik Dauer: 12 Juli 2016 → 19 Juli 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.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Information systems
- Sozialwissenschaften (insg.)
- Geografie, Planung und Entwicklung
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in: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, Jahrgang XLI-B3, 10.06.2016, S. 841-848.
Publikation: Beitrag in Fachzeitschrift › Konferenzaufsatz in Fachzeitschrift › Forschung › 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 -