- Open Access
Window detection in facade images for risk assessment in tunneling
© The Author(s) 2018
- Received: 18 September 2017
- Accepted: 5 January 2018
- Published: 19 January 2018
Settlements induced by tunneling in inner urban areas can easily damage above ground structures. This already has to be considered in early planning of tunneling routes. Assessing the risk of damages to structures on hypothetical tunneling routes inflicted by such settlements beforehand enables routes’ comparability. Hereby, it facilitates the choice of the optimal tunneling route in terms of potential damages and of suitable countermeasures. Risk analyses of structures establishing the assessment obtain relevant data from various sources. Some data even has to be gathered manually. Virtual building models could ease this process and facilitate analyses for entire districts as they combine several required information in a single data set. Commonly, these are yet modelled very coarse. Relevant details like facade openings, which highly affect a structures stiffness, are not included.
In this paper, we propose a system which detects windows in facade images. This is used to subsequently enrich existing virtual building models allowing for a precise risk assessment. For this, we apply a sliding window detector which employs a cascaded classifier to obtain windows in images patches.
Our system yields sufficient results on facade images of several countries showing its general applicability despite regional and architectural variation in the facades’ and windows’ appearance. In an ensuing case study, we assess the risk of damages to structures based on detections of our system using different analysis methods.
We contrast these results to assessments using manually gathered data. Hereby, we show that the detection rate of our proposed system is sufficient for a reliable estimation of a structure’s damage class.
- Window detection
- Cascaded classifier
- Damage risk assessment
Images of facades are publicly available for almost all urban areas from web services like Google Street View or can easily be gathered. For this reason, we propose a pattern recognition approach to detect windows in facade images. This allows for automatically inferring the opening-ratio from existing structures. Through this, required information can be provided to risk analyses with significantly less effort. Accordingly, alternative tunneling routes can be evaluated already in early phases of planning which enables the selection of an optimal route. It can be taken to be optimal if it minimizes structural damages to buildings inflicted by settlements. Structural damages are considered relevant if they cause a tilt and potentially induce cracks in structural and non-structural members that impair appearance, serviceability or even bearing capacities.
The remainder of this paper is organized as follows: In section “Related work” we discuss techniques for risk analyses as well as previously made window detection approaches. Section “Methods” gives insight into our detection system consisting of a soft cascaded classifier (see section “Soft cascaded classifier”) in combination with a sliding window detector (see section “Sliding window detector”). In section “Results” we evaluate the performance of our proposed system and discuss its limitations. The obtained insights and results are then used in the context of a case study (section “Case study”) to test our detection system and risk analyses on real data from a tunneling scenario of the reference subway project Wehrhahn-Linie (WHL) in Düsseldorf, Germany. In the course of this, we compare our results to common methods and highlight the advantages with respect to nowadays idealization of structures. Finally, we conclude our findings and provide an outlook (see section “Discussion”).
For damage assessment of tunneling induced settlements a variety of established methods is recently at hand; (Obel et al. 2017) includes a summary. In short, section “Damage risk assessment” recalls the basics regarding application.
Window detection is a challenging task which has not sufficiently been solved yet. Although it is of high relevance for several research areas, it is mostly referred to as a subtask of 3D building reconstruction. In recent decades a comprehensive body of literature arose concerning the 3D reconstruction of existing buildings. Approaches made are highly versatile. In section “Building reconstruction” we discuss the suitability of different kinds of input data with respect to window detection. Furthermore, we outline previously made approaches to window detection and address their application areas and limitations.
Damage risk assessment
Relation of damage category and limiting tensile strain, according to Boscardin and Cording (1989)
Category of damage
Degree of severity
Limiting strain [%]
Stiffness reduction factor f α according to opening ratio (Neugebauer et al. 2015)
Opening ratio (OR)[%]
Stiffness reduction factor
In case of deep beam models damage of facades is typically assessed employing plane finite elements since it is necessary to consider details to gain a realistic prognosis of the total settlement induced strain distribution. Simultaneously it comes along with higher numerical effort concerning modelling and computation which is justified only in case of important structures having a significant damage potential as it is with low coverage.
Musialski et al. (2013) provide the most encompassing survey of building reconstruction that also comprises methods based on terrestrial imagery. While pattern recognition, matching, and facade parsing approaches are discussed, the detection of facade elements in facade images taken from the ground perspective is only mentioned briefly. Since ground perspective imagery of facades can be gathered with low effort even for larger areas or alternatively can be received from web services like Google Street View, image based window detection approaches qualify best for our purpose. Preliminary work by Neuhausen et. al (2016) focused on a juxtaposition of the most promising approaches concerning window detection in facade images taken from the ground. The discussed methods are divided into three categories: Grammar-based, image processing only, and machine learning aided methods.
Grammar-based methods apply formal grammars to facade images splitting these into increasingly smaller regions until the facade is decomposed into its elements. Ripperda (2008) and Ripperda and Brenner (2009) express a simple grammar based on symmetry and repetition to subdivide facade images. Teboul et al. (2010), on the contrary, develop a detailed shape grammar which also models semantic relationships between certain elements. For this purpose they introduce rules, amongst others, to substitute the ground floor by shops and doors or to split the attic into roof and windows. As can be seen, in general, defining an adequate set of rules is non-trivial and presumes prior knowledge about the expected architecture. Furthermore, grammars allow numerous possible decompositions for a facade. Sampling methods like Markov Chain Monte Carlo (Ripperda and Brenner 2006) or parsing algorithms (Riemenschneider et al. 2012) have to be applied to identify the most probable subdivision. Such methods drastically increase in complexity with the number of rules within the set. Accordingly, sets have to be kept small to be applicable denying a high detailed modeling of relationships between facade elements.
Pattern recognition approaches divide into further two categories. Methods using only image processing rely, similar to grammar-based approaches, on prior knowledge and assumptions about the facades’ appearance. Assuming that windows are aligned grid-like, Lee and Nevatia (2004) superimpose histograms of horizontal and vertical edges in rectified facade images. As result peaks emerge at windows’ locations. Meixner et al. (2011) resumed their work and figured out that it works well on highly regular facades but fails for complex facades with asymmetric window patterns or extensions like balconies or awnings. This illustrates how assumptions and contributed prior knowledge may narrow the field of application. The appearance of facades highly alters in different countries and may even vary between adjacent urban areas. Detection algorithms based on those assumptions have to be adjusted to particular conditions. This involves high effort and raises the need for experts. It would be desirable to have a more general solution.
Machine learning techniques meet this requirement as they neither rely on assumptions on the windows’ alignment nor on prior knowledge about the architecture. Windows can be detected by image features which represent their inherent characteristics. In this context, Haugeard et al. (2009) proposed an approach classifying windows by their edges using a support vector machine with an inexact graph matching kernel. Such methods require an explicitly given feature vector. Alternatively, boosted classifiers as proposed by Viola and Jones (2004) avoid this by choosing a subset from a pool of features. Its practicability and limitations for window detection tasks are investigated by the work of Ali et al. (2007).
Previous approaches either address large facades with highly regular facade element patterns or provide low detection rates whereas in the context of urban tunneling the investigation of small houses with irregular facades is not uncommon. A sufficient approach, thus, has to cope with this issue. Furthermore, a high detection rate is desirable to derive reliable idealizations of structures for further analyses and a precise risk assessment.
A boosted cascade of classifiers has already been applied to the window detection task by Ali et al. (2007) via the Viola-Jones object detection framework (Viola and Jones 2004) albeit the reported detection rate is rather low. Bourdev and Brandt (2005) developed a soft cascaded classifier which, in general, improves the detection rate over the Viola-Jones framework and is more robust regarding a high variability of positive samples. Their classifier, additionally, relies on less features compared to the one of Viola and Jones at similar detection rates. Based on these findings, we decide for a soft cascaded approach which is described in detail in section “Soft cascaded classifier”.
Both, the Viola-Jones object detection framework and the soft cascaded classifier, were originally developed for face detection and yield high accuracy in this field. Beyond this, these classifiers also proved to be successful in further application areas like traffic light detection (Michael and Schlipsing 2015). An application to other areas is, thus, generally possible. However, this requires the objects to be detected to possess a sufficient amount of well separating features as classification relies on many cascaded stages containing several image features. The fact that windows are poor in those features, hence, may complicate the detection.
Soft cascaded classifier
Sliding window detector
The soft cascaded classifier only accepts image patches. For those, the classifier determines if it is a window. A detector is needed which passes relevant image patches to the classifier and further processes the provided classifications. Due to the cascading layout of our classifier non-window patches on average can be rejected very fast. For this reason, an optimized detection algorithm is unnecessary.
We split the evaluation of our detection system into two experiments. In section “Detection quality” we examine the quality of the detection results by means of a calibrated classifier. Following the setup of Ali et al. (2007) we investigate the accuracy of our detections by determining the overlap of the ground truth data with our detection results. As the calibrated classifier makes a compromise such that the detection rate decreases to the benefit of a low false positive rate, we additionally evaluate the trained classifier in a second experiment (see section “Detection distribution”). For that, we steadily increase the classification threshold and observe the resulting distribution of the detections.
To provide comparability we proceed analogously to the single window method of the evaluation framework proposed by Ali et al. (2007) for both experiments. According to this, a detection will only be marked as true positive if it is inside the ground truth label’s rectangle or marginally exceeds the boundaries and covers the label’s area to at least a certain extent. On the contrary, a detection will be marked as false positives if it covers less than 5% of the ground truth label. Since detections have to be as exact as possible to guarantee accurate risk analyses unlike Ali et al. (2007) we constrain exclusively to an overlap of at least 75% between detection and ground truth for true positives.
In previous experiments (Neuhausen et al. 2017), we found that the classification results improve if samples are constrained to be rectified. For training and calibration we use the CMP-base facade database (Radim Tyleček 2013) consisting of 387 rectified images of planar facades without substantial occlusion by vegetation or man-made objects. Images were taken in various countries which is necessary as the windows’ appearance differ between countries and it is desirable to obtain an universal classifier that can be applied in tunneling projects around the world. For the training phase we randomly choose 5000 windows and initially provide as many negative samples generated from parts of the images containing no windows. Similarly, we randomly choose 2000 positive samples and as many non-windows for the calibration phase.
Number of facade images per country we use to evaluate our detection system
# Facade images
Detection results of the calibrated classifier on facades of diverse countries
True positives [%]
False positives [%]
The bell-like shapes of the distributions emerge from the subsequent merging of the positively classified regions. The smaller the threshold, the more regions are positively classified. Consequently, more regions overlap which results in crucially less remaining regions after merging. Especially due to a higher scale and translation invariance at lower classification thresholds, nearby correctly classified regions often overlap heavily. The merged region, thus, is shifted between those owing to the averaging strategy of the merging process. This reduces the detection rate for small thresholds while simultaneously increasing the false positive rate. Reliable results can be expected only for thresholds from 0.54.
Except for few outliers of the Greek image set on thresholds higher than 0.6, the particular distributions for each country’s image set closely resemble eachother. The outliers can be disregarded as they are due to the small number of images within the Greek set. The similarity of the distributions indicates that the window concept was learned properly so that the classifier relies on general image features which recur in image patches of windows regardless of the country. The detection rates of the calibrated classifier (see Table 4) are close to the maximum detection rates of the trained classifier but often shifted towards a slightly higher threshold. This reduces the detection rate but results in a lower false detection rate.
In the context of our reference subway project Wehrhahnlinie (WHL) in Düsseldorf, Germany, we examine three representative structures. These are typical inner-urban masonry houses with different facades and individual opening-ratios in mixed usage with shops and restaurants in ground and first floors while in upper floors offices or residential use pre-dominates. For comparability of results equivalent material parameters are used throughout.
We apply our window detection system to rectified images of the chosen structures’ facades to determine their opening-ratios. Since we presuppose a facade completely filling the image we infer the opening-ratio of a facade from the ratio of detection areas to the image area. Similar to the evaluation in section “Results” for each facade, we provide a distribution of opening-ratios over the classification threshold additionally to the particular responses of our system in case of a calibrated classifier. As the calibrated classifier misses some windows but yields reasonably low false positives rates its results can be interpreted as the minimum opening-ratio a facade definitely possesses. By means of the distribution we identify a range within which the actual opening-ratio lies. This facilitates the categorization of facades into their most probable damage class.
Influence of detected opening layout
Since detection delivers a scalar factor for the opening-ratio in facades only and lacks information on individual sizes and window positions, the impact of randomly generated samples of openings in facades on the maximum strains is analyzed in numerical simulation. Therefore, the opening-ratio is kept constant while the distribution and size of windows in the facade is varied. As representative, an opening-ratio of about 23% is assumed which equals the global mean of two values gained for our three reference structures by detection. For every structure a lower and an upper value of the opening-ratio is detected. The first is the distribution’s maximum while the second one corresponds to the calbirated system (cf. Fig. 17).
For sure, these results give insight into the mechanics of deep beams with a variable number of regular openings, but still lack practical relevance. Thus several more realistic configurations have been simulated in a second step. These configurations are characterized by irregular grids of windows, similar/equivalent opening-ratios and possess two or three floors typical for houses of 7.5 m height. Here, the openings are distributed randomly preserving minimum vertical and horizontal distances of 0.30 m to neighbors. The results indicated by x in Fig. 19 (b) slightly deviate from the general tendency lines and scatter due to random properties. However, interpretation is as follows: If only the opening-ratio is available but information on the exact sizes and locations lacks, the distribution of openings might be idealized to gain maximum numerical strains with a deviation of about ±25% calculated for an opening-ratio of 23%.
Damage risk assessment
Geometry and parameters of the structures
Detected OR (mean)
Evidently the strains obtained from numerical simulation are much smaller than the analytical ones and thus smaller damage categories are predicted, too. Generally, this is traced back to more realistically covered material properties and the soil-structure interaction considered by numerical simulation.
Analytical results employing detected or exact opening-ratios with the LTSM are close and differ one damage category at most. Similar holds true for numerical results when the scatter of unknown window locations is consequently included.
Due to correlation of opening-ratio and strains, the generally smaller opening-ratios from detection always deliver smaller strains and hence assigned damage categories. Applying LTSM the stiffness is reduced little by a small opening-ratio (cf. Table 2) just as the distribution of strains is in numerical simulation.
Risk assessment of tunneling induced damages to existing structures is essential for planning an optimal tunnel route in the urban area. Hereby, damages and the resulting costs of a subsequent maintenance are minimized. Besides the dimensions of a structure, for a precise risk assessment the opening-ratio plays a major role as it directly affects a structure’s stiffness. Deriving this manually from construction plans or by inspections conducted by surveyors is highly cost and time consuming and even not feasible for the vast amount of structures along potential tunneling routes. Virtual building models which could automate this process are yet publicly available but usually lack information about the relevant openings. As windows are the major reason for openings in facades, in this paper we proposed a system to detect windows in facade images. We trained and calibrated a soft cascaded classifier using rectified facade images gathered from different countries to avoid constraining our system to a specific one. A naïve sliding window detector which passes image patches to the classifier and merges overlapping detections is used to scan facade images. We showed that our system achieves detection rates of over 82% in various countries while only exhibiting a false positive rate of 2% on average. For risk assessment these rates are satisfactory to reliably estimate the damage class of a building. Another experiment reveals that the detection rate can be slightly increased entailing an increase of the false positive rate. In our case study we exploit this to define a lower and upper bound for the opening-ratio of a given facade.
With respect to damage assessment LTSM delivers more conservative results compared to numerical simulation by finite elements. The two approaches may differ up to three damage categories. Our detected opening-ratios are well-suited for a quick pre-assessment of settlement induced damages since they deviate from results employing true opening-ratios by one category at most. Exact positions of openings in the facade are of minor interest. It is sufficient to idealize the openings in regular grids over the facade respecting distances to neighbors of about 0.30 m while the number of floors is estimated from the buildings total height. Then automated detection of opening-factors delivers adequately precise results for damage assessment. Much effort might be saved neglecting exact positions and sizes of individual openings.
As the case study demonstrates, our approach yields promising results and is already applicable to aid the risk assessment of tunneling projects in terms of a pre-assessment. This ejects irrelevant structures from further time-consuming analyses. Provided opening-ratios, however, do not satisfy the demands of a precise assessment. To obtain more accurate opening-ratios the system’s detection rate and accuracy have to be increased. A postprocessing based on already detected windows would be desirable to enhance the detection results. The dimensions of present detections could be refined towards actual window edges in the image to improve accuracy. For increasing the detection rate, windows with less image evidence and, hence, lower feature responses have to be taken into account. Decreasing the detection threshold of the classifier would allow the classifier to detect such windows but would also dramatically increase the amount of false positives if applied to the entire image. This classifier should preferably only be applied to image regions which most likely contain windows to keep the false positive rate low. Positions of further potential windows could be derived from the alignment of present detections. A less strictly calibrated classifier could, then, be applied solely to these positions revealing further windows. Moreover, as mentioned before our system is not capable of detecting store windows. Further research has to be done to identify image features which reliably characterize such windows.
Financial support was provided by the German Research Foundation (DFG) in the framework of the subproject D3 of the Collaborative Research Center SFB 837 “Interaction Modeling in Mechanized Tunneling”.
The authors declare that they have not received any external funding.
Availability of data and material
All data sets used in the context of this paper were preexisting. Those are available from the websites of their creators. For CMP-base façade database this is http://cmp.felk.cvut.cz/~tylecr1/facade/, for Ecole Central Paris facades Database this is http://vision.mas.ecp.fr/Personnel/teboul/data.php.
All authors contributed extensively to the work presented in this paper. Neuhausen reviewed the computer vision related literature and developed the methodology of the detection system. Obel did the literature review and methodology for risk assessment. Both elaborated the case study and concluded their findings. Mark and König supervised the entire process of this study. All authors read and approved the final manuscript.
The authors declare that they have no competing interests.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
- Attewell, PB, Yeats, J, Selby, AR. (1986). Soil Movements Induced by Tunneling and Their Effects on Pipelines and Structures. Glasgow: Blackie.Google Scholar
- Ali, H, Seifert, C, Jindal, N, Paletta, L, Paar, G (2007). Window Detection in Facades. In Proceedings of the 14th Conference on Image Analysis and Processing(pp. 837–842). IEEE Computer Society. https://doi.org/10.1109/ICIAP.2007.4362880.
- Baltsavias, EP (2004). Object extraction and revision by image analysis using existing geodata and knowledge: current status and steps towards operational systems. ISPRS Journal of Photogrammetry and Remote Sensing, 58, 129–151.View ArticleGoogle Scholar
- Barber, D, Mills, J, Smith-Voysey, S (2008). Geometric validation of a ground-based mobile laser scanning system. ISPRS Journal of Photogrammetry and Remote Sensing, 61(1), 128–141.View ArticleGoogle Scholar
- Boscardin, MD, & Cording, EJ (1989). Building Response to Excavation Induced Settlement. Journal of Geotechnical Engineering, 115, 1–21.View ArticleGoogle Scholar
- Bourdev, L, & Brandt, J (2005). Robust Object Detection Via Soft Cascade. In Proceedings of the Conference on Computer Vision and Pattern Recognition. IEEE Computer Society, (pp. 236–243).Google Scholar
- Burland, JB, & Wroth, CP (1974). Settlement of buildings and associated damage. In Proceedings of theConference on Settlement of Structures. PentechPress Ltd., London, Cambridge, (pp. 611–654).Google Scholar
- Brenner, C (2005). Building reconstruction from images and laser scanning. International Journal of Applied Earth Observation and Geoinformation, 6(3-4), 187–198.View ArticleGoogle Scholar
- Haala, N, Peter, M, Kremer, J, Hunter, G (2008). Mobile LiDAR mapping for 3D point cloud collection in urban areas – a performance test. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 37, 1119–1124.Google Scholar
- Haala, N, & Kada, M (2010). An update on automatic 3D building reconstruction. ISPRS Journal of Photogrammetry and Remote Sensing, 65, 570–580.View ArticleGoogle Scholar
- Haugeard, J, Philipp-Foliguet, S, Precioso, F, Lebrun, J (2009). Extraction of Windows in facade using Kernel on Graph of Contours. In Proceedings of the 16th Scandinavian Conference on Image Analysis. Springer, Berlin, (pp. 646–656).Google Scholar
- Kämper, C, Putke, T, Zhao, C, Lavasan, AA, Barciaga, T, Mark, P, Schanz, T (2016). Vergleichsrechnungen zu Modellierungsvarianten für Tunnel mit Tübbingauskleidung. Bautechnik, 93(7), 421–432. https://doi.org/10.1002/bate.201500064.
- Lee, SC, & Nevatia, R (2004). Extraction and Integration of Window in a 3D Building Model from Ground View images. In Proceedings of the Conference on Computer Vision and Pattern Recognition. IEEE Computer Society, (pp. 106–113).Google Scholar
- Lienhart, R, Kuranov, A, Pisarevsky, V (2003). Empirical Analysis of Detection Cascades of Boosted Classifiers for Rapid Object Detection. In: Michaelis, B, & Krell, G (Eds.) In Pattern Recognition. DAGM 2003. Lecture Notes in Computer Sciencevol. 2781, (pp. 297–304). Berlin, Heidelberg: Springer. https://doi.org/10.1007/978-3-540-45243-0_39.
- Mark, P, & Schütgen, B (2001). Grenzen elastischen Materialverhaltens von Beton. Beton- und Stahlbetonbau, 96(5), 373–378. https://doi.org/10.1002/best.200100400.
- Mark, P, Niemeier, W, Schindler, S, Blome, A, Heek, P, Krivenko, A, Ziem, E (2012). Radarinterferometrie zum Setzungsmonitoring beim Tunnelbau: Anwendung am Beispiel der Wehrhahn-Linie in Düsseldorf. Bautechnik, 89(11), 764–776. https://doi.org/10.1002/bate.201200035.
- Musialski, P, Wonka, P, Aliaga, DG, Wimmer, M, van Gool, L, Purgathofer, W (2013). A survey of urban reconstruction. Computer Graphics Forum, 32(6), 146–177.View ArticleGoogle Scholar
- Meixner, P, Leberl, F, Brédif, M (2011). Interpretation of 2D and 3D Building Details on Facades and Roofs. In Proceedings of the Conference on Photogrammetric Image Analysis.. ISPRS, (pp. 137–142)Google Scholar
- Michael, M, & Schlipsing, M (2015). Extending Traffic Light Recognition: Efficient Classification of Phase and Pictogram. In Proceedings of the International Joint Conference on Neural Networks. IEEE. https://doi.org/10.1109/IJCNN.2015.7280499.
- Neugebauer, P, Schindler, S, Pähler, I, Blome, A, Mark, P (2015). Präventives Schädigungsmanagement im Tunnelbau – Schutz der oberirdischen Bebauung. In Taschenbuch Für Den Tunnelbau 2015(pp. s318–361). Berlin: Ernst und Sohn. https://doi.org/10.1002/9783433630006.ch10.
- Neuhausen, M, Koch, C, König, M (2016). Image-Based Window Detection - An Overview. In Proceedings of Workshop of the European Group for Intelligent Computing in Engineering.Google Scholar
- Neuhausen, M, Martin, A, Obel, M, Mark, P, König, M (2017). A Cascaded Classifier Approach to Window Detection in Facade Images. In Proceedings of the International Symposium on Automation and Robotics in Construction, Taipei, Taiwan. IAARC.Google Scholar
- Obel, M, Ahrens, MA, Mark, P (2017). Settlement risk assessment for existent structures during mechanized tunneling based on uncertain data. In Proceeding of the 4th International Conference on Computational Methods in Tunneling and Subsurface Engineering. Studia, Innsbruck.Google Scholar
- Peck, RB (1969). Deep excavation and tunneling in soft ground. In Proceedings of the 7th International Conference on Soil Mechanics and Foundation Engineering. Sociedad Mexicana de Mecanica, Mexico City, (pp. 225–260).Google Scholar
- Pu, S, & Vosselman, G (2009). Knowledge based reconstruction of building models from terrestrial laser scanning data. ISPRS Journal of Photogrammetry and Remote Sensing, 64(6), 575–584.View ArticleGoogle Scholar
- Radim Tyleček, RŠ (2013). Spatial Pattern Templates for Recognition of Objects with Regular Structure. In Proceedings of the German Conference on Pattern Recognition. Springer, Berlin.Google Scholar
- Ripperda, N (2008). Grammar based facade reconstruction using rjMCMC. Photogrammetrie Fernerkundung Geoinformation, 2, 83–92.Google Scholar
- Ripperda, N, & Brenner, C (2006). Reconstruction of Facade Structures Using a Formal Grammar and RjMCMC. In: Franke, K, Müller, K-R, Nickolay, B, Schäfer, R (Eds.) In Pattern Recognition: Proceedings of the 28th DAGM Symposium. Springer, Berlin, Germany, (pp. 750–759).View ArticleGoogle Scholar
- Ripperda, N, & Brenner, C (2009). Application of a Formal Grammar to Facade Reconstruction in Semiautomatic and Automatic Environments. In Proceedings of the AGILE Conference on Geographic Information Science.Google Scholar
- Riemenschneider, H, Krispel, U, Thaller, W, Donoser, M, Havemann, S, Fellner, D, Bischof, H (2012). Irregular lattices for complex shape grammar facade parsing. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE, (pp. 1640–1647).Google Scholar
- Schindler, S (2014). Monitoringbasierte strukturmechanische Schadensanalyse von Bauwerken beim Tunnelbau. Dissertation, Ruhr-University Bochum.Google Scholar
- Schindler, S, Hegemann, F, Koch, C, König, M, Mark, P (2016). Radar interferometry based settlement monitoring in tunnelling: Visualisation and accuracy analyses. Visualization in Engineering, 4(7), 1–16. https://doi.org/10.1186/s40327-016-0034-x.
- Teboul, O, Simon, L, Koutsourakis, P, Paragios, N (2010). Segmentation of Building Facades Using Procedural Shape Priors. In Proceedings of the Conference on Computer Vision and Pattern Recognition. IEEE Computer Society, (pp. 3105–3112).Google Scholar
- Viola, P, & Jones, MJ (2004). Robust real-time face detection. International Journal of Computer Vision, 57(2), 137–154. https://doi.org/10.1023/B:VISI.0000013087.49260.fb.
- Wendel, A, Donoser, M, Bischof, H (2010). Unsupervised facade segmentation using repetitive patterns. In Proceedings of the Joint Pattern Recognition Symposium. Springer, Berlin, (pp. 51–60).Google Scholar