Window detection in complex facades

Recky, Michal; Leberl, Franz

doi:10.1109/euvip.2010.5699128

Cited by 15 publications

(10 citation statements)

References 6 publications

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“…The developed method exploits the presence of vertical and horizontal elements on the rectified patch to quickly analyse the façade. The gradient information has previously been used in contributions aiming at façade decomposition (Recky and Leberl 2010;Teeravech et al 2014). In this case, the objective is to early select patches in which the gradient information indicates the patches that are candidates for damage.…”

Section: Damage Assessment On the Refined Façade Image Patchmentioning

confidence: 99%

Towards a More Efficient Detection of Earthquake Induced Façade Damages Using Oblique Uav Imagery

Duarte¹,

Nex²,

Kerle³

et al. 2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Commission I, ICWG I/IIKEY WORDS: multi-view, damage detection, urban search and rescue, salient object detection, deep learning ABSTRACT:Urban search and rescue (USaR) teams require a fast and thorough building damage assessment, to focus their rescue efforts accordingly. Unmanned aerial vehicles (UAV) are able to capture relevant data in a short time frame and survey otherwise inaccessible areas after a disaster, and have thus been identified as useful when coupled with RGB cameras for façade damage detection. Existing literature focuses on the extraction of 3D and/or image features as cues for damage. However, little attention has been given to the efficiency of the proposed methods which hinders its use in an urban search and rescue context. The framework proposed in this paper aims at a more efficient façade damage detection using UAV multi-view imagery. This was achieved directing all damage classification computations only to the image regions containing the façades, hence discarding the irrelevant areas of the acquired images and consequently reducing the time needed for such task. To accomplish this, a three-step approach is proposed: i) building extraction from the sparse point cloud computed from the nadir images collected in an initial flight; ii) use of the latter as proxy for façade location in the oblique images captured in subsequent flights, and iii) selection of the façade image regions to be fed to a damage classification routine. The results show that the proposed framework successfully reduces the extracted façade image regions to be assessed for damage 6 fold, hence increasing the efficiency of subsequent damage detection routines. The framework was tested on a set of UAV multi-view images over a neighborhood of the city of L'Aquila, Italy, affected in 2009 by an earthquake.

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Section: Damage Assessment On the Refined Façade Image Patchmentioning

confidence: 99%

Towards a More Efficient Detection of Earthquake Induced Façade Damages Using Oblique Uav Imagery

Duarte¹,

Nex²,

Kerle³

et al. 2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…The methods described in [5,6,8] The contribution of this paper is two-fold. First, we propose a novel window detection framework, lifting the detections into 3D space which allows estimating locations, orientations and dimensions in 3D space.…”

Section: Introductionmentioning

confidence: 99%

“…Existing works on window detection overcomes intraclass variability by optimizing the window detection algorithm itself [5,6,7,8], all of them working on single images. The methods described in [5,6,8] The contribution of this paper is two-fold.…”

Section: Introductionmentioning

confidence: 99%

3D window localization on building facades from aerial images

Hödlmoser

2015

2015 IEEE International Conference on Image Processing (ICIP)

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Buildings, the key elements of urban 3D modeling and monitoring or thermographic urban 3D map generation, are mainly represented by their facades. Geometric and semantic properties can directly be derived when knowing a facade's windows locations and dimensions. We present a novel 3D window detection framework where due to the high intra-class variability and the low distinctiveness of windows, the focus of this paper is to not optimize the detector itself, but to exploit redundant detection candidates coming from multiple viewpoints. After detecting the candidates in each image individually and clustering them in 3D space, a Markov Random Field and geometric reasoning are exploited to analyze each cluster and to find out correct window candidates. Experiments show the practicability of the proposed approach which outperforms state-of-the-art 2D detectors on a variety of datasets.

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“…The work by Lee and Nevatia [4] was also extended by Recky and Leberl [6] who used gradient projection to detect different opening types on projected façade images -some with a high perspective distortion. As part of that, the façade was divided into row and column orientations by employing the concepts of gradient projection and local peaks.…”

Section: Introductionmentioning

confidence: 99%

An Integrated Octree-RANSAC Technique for Automated LiDAR Building Data Segmentation for Decorative Buildings

Hamid-Lakzaeian

Laefer

2016

Advances in Visual Computing

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This paper introduces a new method for the automated segmentation of laser scanning data for decorative urban buildings. The method combines octree indexing and RANSAC-two previously established but heretofore not integrated techniques. The approach was successfully applied to terrestrial point clouds of the facades of five highly decorative urban structures for which existing approaches could not provide an automated pipeline. The segmentation technique was relatively efficient and wholly scalable requiring only 1 second per 1,000 points, regardless of the façade's level of ornamentation or non-rectilinearity. While the technique struggled with shallow protrusions, its ability to process a wide range of building types and opening shapes with data densities as low as 400 pts/m 2 demonstrate its inherent potential as part of a large and more sophisticated processing approach.

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Window detection in complex facades

Cited by 15 publications

References 6 publications

Towards a More Efficient Detection of Earthquake Induced Façade Damages Using Oblique Uav Imagery

Towards a More Efficient Detection of Earthquake Induced Façade Damages Using Oblique Uav Imagery

3D window localization on building facades from aerial images

An Integrated Octree-RANSAC Technique for Automated LiDAR Building Data Segmentation for Decorative Buildings

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