Zusammenfassung: | |
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.
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Lizenzbestimmungen: | CC BY 3.0 Unported - http://creativecommons.org/licenses/by/3.0/ |
Publikationstyp: | Article |
Publikationsstatus: | publishedVersion |
Erstveröffentlichung: | 2016 |
Schlagwörter (englisch): | Feature Detection, Global Optimization, Linear Feature, Markov Chain Monte Carlo, Simulated Annealing, Spatial Point Processes, Global optimization, Markov processes, Remote sensing, Simulated annealing, Data terms, Feature detection, Linear configuration, Linear feature, Markov Chain Monte-Carlo, Spatial point process, Feature extraction |
Fachliche Zuordnung (DDC): | 500 | Naturwissenschaften, 520 | Astronomie, Kartographie |
Kontrollierte Schlagwörter: | Konferenzschrift |
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