Automatic extraction of roads from aerial images based on scale space and snakes

Laptev, I.; Mayer, Helmut; Lindeberg, Tony; Eckstein, W.; Steger, Carsten; Baumgärtner, Alexander

doi:10.1007/s001380050121

Cited by 198 publications

(132 citation statements)

References 24 publications

(36 reference statements)

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“…[13][14][15][16][17][18][19] In particular, deformable models, 20,21 such as active-contour (snake), have been widely used to detect the boundary of target tissues and extract their shapes. [22][23][24][25] These models, however, typically fail to correctly detect target tissues when adjacent tissues with similar intensity are present. To correctly segment target tissue layers, piece-wise deformable models (a type of templatematching model) adjust their shape to match the shape of the tissue layer by using intensity values from inside and outside of the model.…”

Section: Introductionmentioning

confidence: 99%

Extracting cardiac shapes and motion of the chick embryo heart outflow tract from four-dimensional optical coherence tomography images

et al. 2012

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Abstract. Recent advances in optical coherence tomography (OCT), and the development of image reconstruction algorithms, enabled four-dimensional (4-D) (three-dimensional imaging over time) imaging of the embryonic heart. To further analyze and quantify the dynamics of cardiac beating, segmentation procedures that can extract the shape of the heart and its motion are needed. Most previous studies analyzed cardiac image sequences using manually extracted shapes and measurements. However, this is time consuming and subject to inter-operator variability. Automated or semi-automated analyses of 4-D cardiac OCT images, although very desirable, are also extremely challenging. This work proposes a robust algorithm to semi automatically detect and track cardiac tissue layers from 4-D OCT images of early (tubular) embryonic hearts. Our algorithm uses a two-dimensional (2-D) deformable double-line model (DLM) to detect target cardiac tissues. The detection algorithm uses a maximum-likelihood estimator and was successfully applied to 4-D in vivo OCT images of the heart outflow tract of day three chicken embryos. The extracted shapes captured the dynamics of the chick embryonic heart outflow tract wall, enabling further analysis of cardiac motion.

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Section: Introductionmentioning

confidence: 99%

Extracting cardiac shapes and motion of the chick embryo heart outflow tract from four-dimensional optical coherence tomography images

et al. 2012

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“…For example, common algorithmic strategies include region growing (Amo et al, 2006;Bicego et al, 2003;Hu et al, 2007;Mena and Malpica, 2005;Tesser and Pavlidis, 2000), segmentation and clustering (Ferchichi and Wang, 2005;Wan et al, 2007), machine learning (Butenuth et al, 2003;Yager and Sowmya, 2003), multi-scale extraction and refinement (Baumgartner and Hinz, 2000;Heipke et al, 1995;Mayer et al, 1998;Steger, 1998), and active contours (Laptev et al, 2000;Peng et al, 2008). These methods tend to work well in rural environments, where color and intensity is relatively distinctive and consistent within roads, and in urban environments when assumptions can be made about the structure of roads (e.g., a semi-regular grid pattern (e.g., Hu et al, 2004;Youn and Bethel, 2004)) and/or a knowledge base and carefully tuned parameters can be provided (e.g., Hinz, 2004).…”

Section: Introductionmentioning

confidence: 99%

Extracting roads from dense point clouds in large scale urban environment

Boyko

Funkhouser

2011

ISPRS Journal of Photogrammetry and Remote Sensing

151

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This paper describes a method for extracting roads from a large scale unstructured 3D point cloud of an urban environment consisting of many superimposed scans taken at different times. Given a road map and a point cloud, our system automatically separates road surfaces from the rest of the point cloud. Starting with an approximate map of the road network given in the form of 2D intersection locations connected by polylines, we first produce a 3D representation of the map by optimizing Cardinal splines to minimize the distances to points of the cloud under continuity constraints. We then divide the road network into independent patches, making it feasible to process a large point cloud with a small in-memory working set. For each patch, we fit a 2D active contour to an attractor function with peaks at small vertical discontinuities to predict the locations of curbs. Finally, we output a set of labeled points, where points lying within the active contour are tagged as "road" and the others are not. During experiments with a LIDAR point set containing almost a billion points spread over six square kilometers of a city center, our method provides 86% correctness and 94% completeness.

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“…Bicego et al (2003) use a road tracking method with an 'inertia' term that allows a road extremity to extend a short distance despite lack of support from the data, but do not address gaps as such. A number of methods attempt to close gaps in the extracted network after the fact: Laptev et al (2000) use ziplock snakes to connect gap endpoints, while Zhang et al (1999) use morphological operators. Tupin et al (1998) construct a Markov random field on a graph whose nodes represent line segments, the field labelling the segments as 'road' or 'non-road'.…”

Section: Introductionmentioning

confidence: 99%

Higher-Order Active Contour Energies for Gap Closure

2007

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The full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. ABSTRACT. One of the main difficulties in extracting line networks from images, and in particular road networks from remote sensing images, is the existence of interruptions in the data caused, for example, by occlusions. These can lead to gaps in the extracted network that do not correspond to gaps in the real network. In this paper, we describe a higher-order active contour energy that in addition to favouring network-like regions, includes a prior term penalizing networks containing 'nearby opposing extremities', thereby making gaps in the extracted network less likely. The new energy term causes such extremities to attract one another during gradient descent. They thus move towards one another and join, closing the gap. To minimize the energy, we develop specific techniques to handle the high-order derivatives that appear in the gradient descent equation. We present the results of automatic extraction of networks from real remote-sensing images, showing the ability of the model to overcome interruptions.

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Automatic extraction of roads from aerial images based on scale space and snakes

Cited by 198 publications

References 24 publications

Extracting cardiac shapes and motion of the chick embryo heart outflow tract from four-dimensional optical coherence tomography images

Extracting cardiac shapes and motion of the chick embryo heart outflow tract from four-dimensional optical coherence tomography images

Extracting roads from dense point clouds in large scale urban environment

Higher-Order Active Contour Energies for Gap Closure

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