{"title":"Finding Poly-Curves of Straight Line and Ellipse Segments in Images Segmentierung von Pixelketten in Geraden- und Ellipsenelemente","authors":"Susanne Wenzel, W. Förstner","doi":"10.1127/1432-8364/2013/0178","DOIUrl":"https://doi.org/10.1127/1432-8364/2013/0178","url":null,"abstract":"Simplification of given polygons has attracted many researchers. Especially, finding circular and elliptical structures in images is relevant in many applications. Given pixel chains from edge detection, this paper proposes a method to segment them into straight line and ellipse segments. We propose an adaption of Douglas-Peucker’s polygon simplification algorithm using circle segments instead of straight line segments and partition the sequence of points instead the sequence of edges. It is robust and decreases the complexity of given polygons better than the original algorithm. In a second step, we further simplify the poly-curve by merging neighbouring segments to straight line and ellipse segments. Merging is based on the evaluation of variation of entropy for proposed geometric models, which turns out as a combination of hypothesis testing and model selection. We demonstrate the results of circlePeucker as well as merging on several images of scenes with significant circular structures and compare them with the method of PATRAUCEAN et al. (2012). Zusammenfassung: . ie tion runder und elliptischer Strukturen ist relevant für viele Anwendungen. Die Reduktion der Komplexität gegebener Polygone ist für sich ein interessantes Forschungsthema. Diese Arbeit stellt ein Verfahren zur Segmentierung von Pixelketten einer Kantendetektion in Geradenund Ellipsensegmente vor. Der erste Schritt besteht in einer Adaption des Douglas-Peucker Algorithmus, in der Kreise anstelle von Geraden zur Partitionierung verwendet werden und die Punktstatt der Kantensequenz partitioniert wird. Das Verfahren ist robust und reduziert die Komplexität der gegebenen Polygone stärker als der originale Algorithmus. In einem zweiten Schritt vereinfachen wir diese Vorsegmentierung durch das Verschmelzen benachbarter Segmente zu Geradenund Ellipsensegmenten und stützen uns dabei auf die Entropieänderung. Wir zeigen die Ergebnisse der Vorsegmentierung als auch der folgenden Vereinfachung anhand verschiedener Bilder von Szenen, die signifikante kreisförmige Strukturen aufweisen und vergleichen sie mit dem Algorithmus von PATRAUCEAN et al. (2012).","PeriodicalId":56096,"journal":{"name":"Photogrammetrie Fernerkundung Geoinformation","volume":"11 1","pages":"297-308"},"PeriodicalIF":0.0,"publicationDate":"2013-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90673167","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"DijkstraFPS: Graph Partitioning in Geometry and Image Processing DijkstraFPS: Graphpartitionierung in Geometrie und Bildverarbeitung","authors":"F. Schindler, W. Förstner","doi":"10.1127/1432-8364/2013/0177","DOIUrl":"https://doi.org/10.1127/1432-8364/2013/0177","url":null,"abstract":"","PeriodicalId":56096,"journal":{"name":"Photogrammetrie Fernerkundung Geoinformation","volume":"80 1","pages":"285-296"},"PeriodicalIF":0.0,"publicationDate":"2013-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88794854","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
J. Stoter, H. Ledoux, M. Reuvers, L. V. D. Brink, R. Klooster, P. Janssen, J. Beetz, F. Penninga, G. Vosselman
{"title":"Establishing and implementing a national 3D standard in The Netherlands: Entwicklung und Implementierung eines nationalen 3D Standards in den Niederlanden","authors":"J. Stoter, H. Ledoux, M. Reuvers, L. V. D. Brink, R. Klooster, P. Janssen, J. Beetz, F. Penninga, G. Vosselman","doi":"10.1127/1432-8364/2013/0184","DOIUrl":"https://doi.org/10.1127/1432-8364/2013/0184","url":null,"abstract":"This paper describes the 3D developments achieved within the 3D Pilot NL. The first phase of this pilot (January 2010 June 2011) resulted in a national 3D standard, modeled as CityGML application domain extension (ADE). This standard is briefly explained in this paper. To implement this standard as a nationwide 3D dataset, further research was needed. The second phase of the 3D Pilot finished in December 2012 developed tools, techniques and guidelines to support the implementation of the 3D standard. These are: 1) implementation specifications for the national CityGML ADE to be used in tendering documents, 2) example data compliant to the 3D standard, 3) 3D validator, 4) guidelines to update 3D datasets, and 5) 3D application showcases. These instruments are further explained and presented in this paper. Der Beitrag beschreibt den aktuellen Stand des niederlandischen Projektes 3D Pilot NL und zugehorige Entwicklungen zur 3D-Datenmodellierung. Die erste Phase des Projektes fuhrte von Januar 2010 bis Juni 2011 zu einem nationalen 3D Standard, der als Application Domain Extension (ADE) von CityGML modelliert wurde. Fur seine Implementierung und Anwendung auf einen landesweiten 3D-Datensatz waren weitere Untersuchungen erforderlich. In der im Dezember 2012 beendeten zweiten Phase des 3D Pilot NL wurden Hilfsmittel fur die Implementierung entwickelt: 1.) eine Implementierungsrichtlinie der nationalen Application Domain Extension (ADE) fur City- GML zur Verwendung in Ausschreibungen, 2.) Beispieldatensatze, 3.) ein 3D Validator fur die Konsistenzprufung von Daten atzen, 4.) eine Richtlinie fur die Fortfuhrung der 3D Daten und 5.) Anwendungsbeispiele. Diese Werkzeuge werden hier vorgestellt.","PeriodicalId":56096,"journal":{"name":"Photogrammetrie Fernerkundung Geoinformation","volume":"255 ","pages":"381-392"},"PeriodicalIF":0.0,"publicationDate":"2013-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1127/1432-8364/2013/0184","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72422887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A Trainable Markov Random Field for Low-Level Image Feature Matching with Spatial Relationships Ein trainierbares Markoff-Zufallsfeld für die Zuordnung lokaler Bildmerkmale unter Berücksichtigung ihrer räumlichen Beziehungen","authors":"Timo Dickscheid, W. Förstner","doi":"10.1127/1432-8364/2013/0176","DOIUrl":"https://doi.org/10.1127/1432-8364/2013/0176","url":null,"abstract":"Many vision applications rely on local features for image analysis, notably in the areas of object recognition, image registration and camera calibration. One important example in photogrammetry are fully automatic algorithms for relative image orientation. Such applications rely on a matching algorithm to extract a sufficient number of correct feature correspondences at acceptable outlier rates, which is most often based on the similarity of feature descriptions. When the number of detected features is low, it is advisable to use multiple feature detectors with complementary properties. When feature similarity is not sufficient for matching, spatial feature relationships provide valuable information. In this work, a highly generic matching algorithm is proposed which is based on a trainable Markov random field (MRF). It is able to incorporate almost arbitrary combinations of features, similarity measures and pairwise spatial relationships, and has a clear statistical interpretation. A major novelty is its ability to compensate for weaknesses in one information cue by implicitely exploiting the strengths of others. Zusammenfassung: Ein trainierbares MarkoffZufallsfeld für die Zuordnung lokaler Bildmerkmale unter Berücksichtigung ihrer räumlichen Beziehungen. Viele Anwendungen im Bereich des maschinellen Sehens nutzen lokale Merkmale für die Bildanalyse, insbesondere in den Bereichen Objekterkennung, Bildregistrierung und Kamerakalibrierung. Ein wichtiges Beispiel in der Photogrammetrie sind vollautomatische Algorithmen für die relative Kameraorientierung. Dazu muss aus den Bildmerkmalen verschiedener Bilder anhand eines Matchingalgorithmus eine ausreichende Anzahl von Zuordnungen mit vertretbarem Ausreißeranteil gewonnen werden. Die Suche nach Zuordnungen basiert dabei meist auf der Ähnlichkeit von Merkmalsbeschreibungen. Wenn die Anzahl der extrahierten Merkmale gering ist, macht es Sinn, mehrere möglichst komplementäre Merkmalsdetektoren gleichzeitig einzusetzen. Ist die Ähnlichkeit von Bildmerkmalen kein ausreichendes Kriterium für die Zuordnung, liefern räumliche Beziehungen von Merkmalen zusätzlich wertvolle Information. In dieser Arbeit stellen wir ein allgemeines Matchingverfahren vor, das auf einem trainierbaren Markoff-Zufallsfeld basiert. Es ermöglicht die gleichzeitige Berücksichtigung nahezu beliebiger Arten von Bildmerkmalen, Ähnlichkeitsmaßen und paarweisen räumlichen Beziehungen, und lässt sich statistisch klar interpretieren. Eine Besonderheit dieses Verfahrens ist seine Eigenschaft, Schwachpunkte einer Informationsquelle durch die Stärken einer anderen implizit auszugleichen.","PeriodicalId":56096,"journal":{"name":"Photogrammetrie Fernerkundung Geoinformation","volume":"9 1","pages":"269-283"},"PeriodicalIF":0.0,"publicationDate":"2013-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88432850","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Lutz Bannehr, A. Schmidt, Johannes Piechel, Thomas Luhmann
{"title":"Extracting Urban Parameters of the City of Oldenburg from Hyperspectral, Thermal, and Airborne Laser Scanning Data Ableitung von städtischen Parametern der Stadt Oldenburg durch Hyperspektral-, Thermal- und Airborne Laser Scanning Daten","authors":"Lutz Bannehr, A. Schmidt, Johannes Piechel, Thomas Luhmann","doi":"10.1127/1432-8364/2013/0183","DOIUrl":"https://doi.org/10.1127/1432-8364/2013/0183","url":null,"abstract":"","PeriodicalId":56096,"journal":{"name":"Photogrammetrie Fernerkundung Geoinformation","volume":"35 1","pages":"367-379"},"PeriodicalIF":0.0,"publicationDate":"2013-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77326193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Graphical Models in Geodesy and Photogrammetry Graphische Modelle in Geodäsie und Photogrammetrie","authors":"W. Förstner","doi":"10.1127/1432-8364/2013/0175","DOIUrl":"https://doi.org/10.1127/1432-8364/2013/0175","url":null,"abstract":"","PeriodicalId":56096,"journal":{"name":"Photogrammetrie Fernerkundung Geoinformation","volume":"109 1","pages":"255-267"},"PeriodicalIF":0.0,"publicationDate":"2013-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81197410","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A Robust Iterative Kalman Filter Based On Implicit Measurement Equations Robuster iterativer Kalman-Filter mit implizierten Beobachtungsgleichungen","authors":"Richard Steffen","doi":"10.1127/1432-8364/2013/0180","DOIUrl":"https://doi.org/10.1127/1432-8364/2013/0180","url":null,"abstract":"In the field of robotics and computer vision recursive estimation of time dependent processes is one of the key tasks. Usually Kalman filter based techniques are used, which rely on explicit model functions, that directly and explicitly describe the effect of the parameters on the observations. However, some problems naturally result in implicit constraints between the observations and the parameters, for instance all those resulting in homogeneous equation systems. By implicit we mean, that the constraints are given by equations, that are not easily solvable for the observation vector. We derive an iterative extended Kalman filter framework based on implicit measurement equations. In a wide field of applications the possibility to use implicit constraints simplifies the process of specifying suitable measurement equations. As an extension we introduce a robustification technique similar to [17] and [8], which allows the presented estimation scheme to cope with outliers. Furthermore we will present results for the application of the proposed framework to the structure-from-motion task in the case of an image sequence acquired by an airborne vehicle.","PeriodicalId":56096,"journal":{"name":"Photogrammetrie Fernerkundung Geoinformation","volume":"77 1","pages":"323-332"},"PeriodicalIF":0.0,"publicationDate":"2013-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83864172","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Bundle Adjustment and System Calibration with Points at Infinity for Omnidirectional Camera Systems Bündelausgleichung und Systemkalibrierung mit Punkten im Unendlichen für omnidirektionale Kamerasysteme","authors":"J. Schneider, Wolfgang Förstner","doi":"10.1127/1432-8364/2013/0179","DOIUrl":"https://doi.org/10.1127/1432-8364/2013/0179","url":null,"abstract":"We present a calibration method for multi-view cameras that provides a rigorous maximum likelihood estimation of the mutual orientation of the cameras within a rigid multi-camera system. No calibration targets are needed, just a movement of the multi-camera system taking synchronized images of a highly textured and static scene. Multi-camera systems with non-overlapping views have to be rotated within the scene so that corresponding points are visible in different cameras at different times of exposure. By using an extended version of the projective collinearity equation all estimates can be optimized in one bundle adjustment where we constrain the relative poses of the cameras to be fixed. For stabilizing camera orientations – especially rotations – one should generally use points at the horizon within the bundle adjustment, which classical bundle adjustment programs are not capable of. We use a minimal representation of homogeneous coordinates for image and scene points which allows us to use images of omnidirectional cameras with single viewpoint like fisheye cameras and scene points at a large distance from the camera or even at infinity. We show results of our calibration method on (1) the omnidirectional multi-camera system Ladybug 3 from Point Grey, (2) a camera-rig with five cameras used for the acquisition of complex 3D structures and (3) a camera-rig mounted on a UAV consisting of four fisheye cameras which provide a large field of view and which is used for visual odometry and obstacle detection in the project MoD (DFG-Project FOR 1505 “Mapping on Demand”). Zusammenfassung: Bündelausgleichung und Systemkalibrierung mit Punkten im Unendlichen für omnidirektionale Kamerasysteme. In diesem Artikel stellen wir eine Kalibrierungsmethode für Multikamerasysteme vor, welche eine strenge MaximumLikelihood-Schätzung der gegenseitigen Orientierungen der Kameras innerhalb eines starren Multikamerasystems ermöglicht. Zielmarken werden nicht benötigt. Das synchronisiert Bilder aufnehmende Kamerasystem muss lediglich in einer stark texturierten statischen Szene bewegt werden. Multikamerasysteme, deren Bilder sich nicht überlappen, werden innerhalb der Szene rotiert, so dass korrespondierende Punkte in jeder Kamera zu unterschiedlichen Aufnahmezeitpunkten sichtbar sind. Unter Verwendung einer erweiterten projektiven Kollinearitätsgleichung können alle zu schätzenden Größen in einer Bündelausgleichung optimiert werden. Zur Stabilisierung der Kameraorientierungen – besonders der Rotationen – sollten Punkte am Horizont in der Bündelausgleichung verwendet werden, wozu klassische Bündelausgleichungsprogramme nicht in der Lage sind. Wir benutzen eine minimale Repräsentation für homogene Koordinaten für Bildund Objektpunkte, welche es uns ermöglicht, mit Bildern omnidirektionaler Kameras wie Fisheye-Kameras und mit Objektpunkten, welche weit entfernt oder im Unendlichen liegen, umzugehen. Wir zeigen Ergebnisse unserer Kalibrierungsmethode für (1) das omnidir","PeriodicalId":56096,"journal":{"name":"Photogrammetrie Fernerkundung Geoinformation","volume":"33 1","pages":"309-321"},"PeriodicalIF":0.0,"publicationDate":"2013-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89699062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}