CMRT09

Stilla, Uwe

Bibliographic data

Monograph

Persistent identifier:: 856955019

Author:: Stilla, Uwe

Title:: CMRT09

Sub title:: object extraction for 3D city models, road databases, and traffic monitoring ; concepts, algorithms and evaluation ; Paris, France, September 3 - 4, 2009 ; [joint conference of ISPRS working groups III/4 and III/5]

Scope:: X, 234 Seiten

Year of publication:: 2009

Place of publication:: Lemmer

Publisher of the original:: GITC

Identifier (digital):: 856955019

Illustration:: Illustrationen, Diagramme, Karten

Language:: English

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2016

Document type:: Monograph

Collection:: Earth sciences

Chapter

Title:: ROAD ROUNDABOUT EXTRACTION FROM VERY HIGH RESOLUTION AERIAL IMAGERY M. Ravenbakhsh, C. S. Fraser

Document type:: Monograph

Structure type:: Chapter

CMRT09: Object Extraction for 3D City Models, Road Databases and Traffic Monitoring - Concepts, Algorithms, and Evaluation similarly to the method described earlier with the exception that the initial curve is defined as a circle around the roundabout node so that it must be placed inside the island (Fig. 7a). (a) (b) Figure 6. Island extraction: (a) Expansion evolution result after 1330 iterations, (b) selected curve (red) and approximated cubic spline (green), (c) other curves resulting from iterative curve evolution, and (d) eventual result of expansion evolution. The diameter of the circle needs to be less than the threshold which dictates whether islands are regarded as point or area objects in the topographic database. By experiment, it is safer to define a circle with a diameter as one-third of this threshold. The expansion result is compared with each group of shrinkage results separately, and points that are close enough to each other are selected. These points are candidates for ellipse fitting. The fitting result for a case with the highest number of points is more likely to produce a correct result of island extraction (Fig. 7f). (d) «=4606 (e) (f) Figure 7. First sequence for island extraction: (a) initial successive circles, 3 located outside central island for shrinking evolution and 1 inside for expansion curve evolution; (b), (c) and (d) results of the shrinking evolution for exterior circles from large to small (n denotes iteration number); (e) result of iterative expansion evolution to interior circle; (f) final result. Extracted central islands are verified using the existing information derived from the topographic database. When a roundabout appears in the database as an area object, as shown in Fig. 3, the diameter of its central island (D1) obtained from the extraction process must only differ from that obtained from vector data (D2) by a small amount. In an ideal situation, the difference (AD) corresponds to the width of the circulating roadway (W), i.e. W=AD. In practice, due to the imprecise digitization of roundabouts, polygonal vector data do not always lie on the middle axis of the circulating roadway, but somewhere within its area. Therefore, AD is expected to be within the range of 0 to 2W, i.e. 0< AD<2W. In the case where a roundabout appears as a point feature, the diameter of the extracted central island must fall within a predefined range whose highest value is the threshold below which a roundabout is regarded as a point object and whose lowest value is the minimum possible diameter for a central island. 3.3 The Snake Model for Roundabout reconstruction The snake model, or parametric active contour method (Kass et al., 1988), used to delineate the roundabout outline is now briefly overviewed to provide a basis for further discussion. Further details are provided in Ravanbakhsh et al. (2008) and Ravanbakhsh (2008). Snakes are especially useful for delineating objects that are hard to model with rigid geometric primitives. They are thus well suited to modeling roundabouts since the borders are of diverse shape with various degrees of curvature. Snakes are polygonal curves associated with an objective function that combines an image term (external energy) and measurement of the image force (e.g. the edge strength). There is also a regularization term (internal energy) and a minimization of the tension and curvature of the polygon. The curve is deformed so as to iteratively optimize the objective function. Traditional snakes are sensitive to noise and need precise initialization. Since roundabout borders have various degrees of curvature, a close initialization cannot often be provided. As a result, traditional snakes can easily get stuck in an undesirable local minimum. To overcome these limitations, the ziplock snake model was developed (Neuenschwander et al., 1997). A ziplock snake consists of two parts: an active part and a passive part. The active part is further divided into two segments, indicated as head and tail, respectively (Fig. 8). The active and passive parts of the ziplock snake are separated by moving force boundaries. Unlike the procedure for a traditional snake, the external force derived from the image is turned on only for the active parts. Thus, the movement of passive vertices is not affected by any image forces. Starting from two short pieces, the active part is iteratively optimized to image features, and the force boundaries are progressively moved towards the centre of the snake. Each time that the force boundaries are moved, the passive part is re-interpolated using the position and direction of the end vertices of the two active segments. Optimization is stopped when force boundaries meet each other. Ziplock snakes need far less initialization effort and are less affected by the shrinking effect from the internal energy term. Furthermore, their computation is more robust because the active part, whose energy is minimized, is always quite close to the contour being extracted. This modified snake model can detect image features even when the initialisation is far away from the solution. However, it can still become confused in the presence of disturbances. In high resolution aerial images, such

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