New experimental techniques for fracture testing of highly deformable materials

Abstract. In the recent past, dense image matching methods such as Semi-Global Matching (SGM) became popular for many applications. The SGM approach has been adapted to and implemented for Leica ADS line-scanner data by North West Geomatics (North West) in co-operation with Leica Geosystems; it is used in North West’s production workflow. One of the advantages of ADS imagery is the calibrated color information (RGB and near infrared), extending SGM-derived point clouds to dense “image point clouds” or, more general, information clouds (info clouds). With the goal of automating the quality control of ADS data, info clouds are utilized for Shear Analysis: Three-dimensional offsets of adjacent ADS image strips are determined from a pattern of info cloud pairs in strip overlaps by point cloud matching. The presented approach integrates geometry (height) and radiometry (intensity) information; matching is based on local point-to-plane distances for all points in a given cloud. The offset is derived in a least squares adjustment by applying it to each individual distance computation equation. Using intensities in addition to heights greatly benefits the offset computation, because intensity gradients tend to occur more frequently than height gradients. They can provide or complement the required information for the derivation of planimetric offset components. The paper details the combined geometric/radiometric point cloud matching approach and verifies the results against manual measurements.

Efficiency and accuracy of semi-global matching (SGM) make it outperform many stereo matching algorithms and is widely used under challenging occasions. However, SGM only incorporates information along a scanline in each pass and lacks interaction between scanlines, resulting in streak artifacts in the disparity image. We introduce a local edge-aware filtering method to SGM to enhance the interaction of neighboring scanlines, since streak artifacts can be avoided. We use bilateral weights based on intensity similarity and spatial affinity between pixels to build connections among scanlines. In each pass, we recursively estimate the aggregated cost of SGM and compute the weighted average of aggregated costs for pixels in the orthogonal direction to obtain the output of our method along each scanline. As one-dimensional bilateral filtering is used in our method, the extra computation is linear to image resolution and label space, which is a small fraction of that needed by SGM. We present ablation studies using stereo pairs under both constrained and natural conditions to verify the effectiveness of our method. Extensive experiments on Middlebury and Karlsruhe Institute of Technology and Toyota Technology Institute datasets demonstrate that our method removes all streak artifacts, improves the quality of the disparity image, and outperforms many other non-local cost aggregation approaches.

Depth information can be obtained using stereo matching algorithms, which compute the horizontal displacement (disparity) of the corresponding points and convert to depth information using the triangular relation. However, the matching process is challenging with presence of textureless regions. This paper proposes a novel disparity refinement method for stereo matching based on Semi-global Matching (SGM) algorithm for texture less images. To be brief, the SGM algorithm is a high-performance matching algorithm of a stereo image pair and it reaches a trade off between matching quality and computing complexity. The main contribution comes from the improvement of matching quality on texture less regions. At the end of SGM, a right-to-left consistency check is performed to remove the invalid pixels in the disparity map. The proposed method is added after the right-to-left consistent check. We assume that the texture less regions in the original stereo pairs are planar. We employ edge detection to extract the textureless regions. The fitting method is employed in horizontal and vertical directions respectively. If the distance of two adjacent edge pixels is sufficiently large, the corresponding line segment will be considered as texturelss. For every texturelss line segment, the RANSAC algorithm is performed on the corresponding line segment in the disparity map. We use the well-known Middlebury dataset to compare our method with the normal SGM and other matching algorithms. It shows that our method performs well for most texture less stereo pairs.

Determination and prediction of crack patterns in hot mix asphalt (HMA) mixtures

In this work we evaluate the use of several real-time dense stereo algorithms as a passive 3D sensing technology for potential use as part of a driver assistance system or autonomous vehicle guidance. A key limitation in prior work in this area is that although significant comparative work has been done on dense stereo algorithms using de facto laboratory test sets only limited work has been done on evaluation in real world environments such as that found in potential automotive usage. This comparative study aims to provide an empirical comparison using automotive environment video imagery and compare this against dense stereo results drawn on standard test sequences in addition to considering the computational requirement against performance in real-time. We evaluate five chosen algorithms: Block Matching, Semi-Global Matching, No-Maximal Disparity, Cross-Based Local Approach, Adaptive Aggregation with Dynamic Programming. Our comparison shows a contrast between the results obtained on standard test sequences and those for automotive application imagery where a Semi-Global Matching approach gave the best empirical performance. From our study we can conclude that the noise present in automotive applications, can impact the quality of the depth information output from more complex algorithms (No-Maximal Disparity, Cross-Based Local Approach, Adaptive Aggregation with Dynamic Programming) resulting that in practice the disparity maps produced are comparable with those of simpler approaches such as Block Matching and Semi-Global Matching which empirically perform better in the automotive environment test sequences. This empirical result on automotive environment data contradicts the comparative result found on standard dense stereo test sequences using a statistical comparison methodology leading to interesting observations regarding current relative evaulation approaches.

Automatic building detection from very-high-resolution (VHR) satellite images is a difficult task. The detection accuracy is usually limited by spectral ambiguities and the uncertainties of the available height information. Feature extraction and training sampling collection for supervised methods are other sources of uncertainty. Most widely used VHR sensors have shorter revisit cycles (IKONOS/GeoEye-1/2, 3 days; WorldView 1/2, 1.1 days) due to large off-nadir viewing angles and hence are able to perform consistent acquisition of mono or stereo images. In this article, we investigate the possibility of using high-temporal stereo VHR images to enhance remote-sensing image interpretation under the context of building detection. Digital surface models, which contain the height information, are generated for each date using semi-global matching. Pre-classification is performed combining the height and spectral information to obtain an initial building probability map. With a reference land cover map available for one date, the training samples of the other dates are automatically derived using a rule-based validating procedure. A spatiotemporal inference filter is developed considering the spectral, spatial, and temporal aspects to enhance the building probability maps. This aims at homogenizing the building probability values of spectrally similar pixels in the spatial domain and geometrically similar pixels in the temporal domain, while being robust to the silhouette of the images and geometric discrepancies of the multitemporal data. The effectiveness and robustness of the proposed method are evaluated by performing three experiments on six stereo pairs of the same region over a time period of five years (2006–2011). The area under curve (AUC) of the receiver operating characteristic and kappa statistic (κ) are employed to assess the results. These experiments show that spatiotemporal inference filtering largely improves the accuracy of the building probability map (average AUC = 0.95) while facilitating building extraction in snow-covered images. The resulting building probability maps can be further used for other applications (e.g. building footprint updating).

Emission reduction effect of eco-friendly asphalt modified with isocyanate-nanoclay composites

The all-embracing inspection of geometry structures of revetments along urban rivers using the conventional field visual inspection is technically complex and time-consuming. In this study, an approach using dense point clouds derived from low-cost unmanned aerial vehicle (UAV) photogrammetry is proposed to automatically and efficiently recognize the signatures of revetment damage. To quickly and accurately recover the finely detailed surface of a revetment, an object space-based dense matching approach, that is, region growing coupled with semi-global matching, is exploited to generate pixel-by-pixel dense point clouds for characterizing the signatures of revetment damage. Then, damage recognition is conducted using a proposed operator, that is, a self-adaptive and multiscale gradient operator, which is designed to extract the damaged regions with different sizes in the slope intensity image of the revetment. A revetment with slope protection along urban rivers is selected to evaluate the performance of damage recognition. Results indicate that the proposed approach can be considered an effective alternative to field visual inspection for revetment damage recognition along urban rivers because our method not only recovers the finely detailed surface of the revetment but also remarkably improves the accuracy of revetment damage recognition.

LiDAR-guided stereo matching with a spatial consistency constraint

Multiple modalities for stereo matching are beneficial for robust path estimation and actioning of autonomous robots in harsh environments, e.g. in the presence of smoke and dust. In order to combine the information resulting from the different modalities, a dense stereo matching approach based on semi-global matching and a combined cost function using cross-based support regions and phase congruency shows a good performance. However, these computationally complex algorithmic steps set high requirements for the mobile processing platform and prohibit a real-time execution at limited power budget on mobile platforms. Therefore, this paper explores the usage of graphic processors for the parallelization and acceleration of the aforementioned algorithm. The resulting implementation performs the computation of phase congruency and cross-based support regions at 68 and 5 frames per second for $[960\mathrm{x}560]$ pixel images on a Nvidia Quadro P5000 and Tegra X2 GPU respectively.

Abstract. The 3D information of road infrastructures are gaining importance with the development of autonomous driving. The exact absolute position and height of lane markings, for example, support lane-accurate localization. Several approaches have been proposed for the 3D reconstruction of line features from multi-view airborne optical imagery. However, standard appearance-based matching approaches for 3D reconstruction are hardly applicable on lane markings due to the similar color profile of all lane markings and the lack of textures in their neighboring areas. We present a workflow for 3D lane markings reconstruction without explicit feature matching process using multi-view aerial imagery. The aim is to optimize the best 3D line location by minimizing the distance from its back projection to the detected 2D line in all the covering images. Firstly, the lane markings are automatically extracted from aerial images using standard line detection algorithms. By projecting these extracted lines onto the Semi-Global Matching (SGM) generated Digital Surface Model (DSM), the approximate 3D line segments are generated. Starting from these approximations, the 3D lines are iteratively refined based on the detected 2D lines in the original images and the viewing geometry. The proposed approach relies on precise detection of 2D lines in image space, a pre-knowledge of the approximate 3D line segments, and it heavily relies on image orientations. Nevertheless, it avoids the problem of non-textured neighborhood and is not limited to lines of finite length. The theoretical precision of 3D reconstruction with the proposed framework is evaluated.

Dense matching plays an important role in many fields, such as DEM (digital evaluation model) producing, robot navigation and 3D environment reconstruction. Traditional approaches may meet the demand of accuracy. But the calculation time and out puts density is hardly be accepted. Focus on the matching efficiency and complex terrain surface matching feasibility an aerial image dense matching method based on optical flow field is proposed in this paper. First, some high accurate and uniformed control points are extracted by using the feature based matching method. Then the optical flow is calculated by using these control points, so as to determine the similar region between two images. Second, the optical flow field is interpolated by using the multi-level B-spline interpolation in the similar region and accomplished the pixel by pixel coarse matching. Final, the results related to the coarse matching refinement based on the combined constraint, which recognizes the same points between images. The experimental results have shown that our method can achieve per-pixel dense matching points, the matching accuracy achieves sub-pixel level, and fully meet the three-dimensional reconstruction and automatic generation of DSM-intensive matching’s requirements. The comparison experiments demonstrated that our approach’s matching efficiency is higher than semi-global matching (SGM) and Patch-based multi-view stereo matching (PMVS) which verifies the feasibility and effectiveness of the algorithm.

Most of the recent top-ranked stereovision algorithms are based on some oversegmentation of the stereo pair images. Each image segment is modelled as a 3D plane, transposing the stereovision problem into a 3-parameters plane estimation problem. Trying to find the set of planes that best fits the input data while regularising on 3D adjacent planes, a global energy is minimised using different optimisation strategies. The regularisation term is important as it enforces the reconstructed scene consistency. In this paper, we propose a disparity map refinement strategy based on a triangular mesh of the image domain. This mesh is projected along the rays of the camera, each triangle being modelled by a slanted plane. Considering triangles instead of free form superpixels, i.e. adding a geometrical constraint on the shape of segment boundaries, allows to formulate simpler regularisation terms that only involve the mesh vertex disparities in the computations. Different curvature regularisation terms of the literature are explored and a new one is proposed, with better properties with regard to the usual coplanarity assumption between adjacent planes. We demonstrate the performance gain of the proposed method compared to the popular Semi-Global Matching (SGM) algorithm.

Aims This research investigates the effect of adding natural Jordanian Zeolitic tuffs to the SUPERPAVE asphalt mixture at the dynamic creep and the indirect tensile performance. Background Rutting and fatigue are considered the most common types of distress that affect pavements all over the world. Many factors can affect these properties, such as different aggregate properties, asphalt grades, and properties, as well as the type and amount of the mineral filler. Mineral fillers play an important role in enhancing the asphalt mixtures' performance by filling the voids in the mixture besides improving the cohesion of the binder as it has a small weight in addition to a very large surface area. Generally, the powder of limestone is the most used type of filler. However, studies showed that many materials could also be used as mineral fillers successfully. The focus of this study is using natural Jordanian Zeolitic tuff as a mineral filler. Jordanian zeolitic tuff mainly consists of Phillipsite, chabazite, and faujasite, which are considered the most abundant Zeolitic tuff minerals. The pyroclastic material is widely distributed in the Badia region of northeast Jordan. Zeolitic tuffs are located in Jabal Aritayn (30km northeast of Azraq), Tlul AlShahba (20 km east of Al Safawi), Tal Al-Rimah (35 km northeast of Al Mafraq), and other small deposits in central and south Jordan. Objectives The aim of this research was to study the impact of Zeolitic tuffs on overall Superpave asphalt mixture performance. Methods The specimens were prepared using the optimum asphalt content obtained by the SUPERPAVE method. Dynamic Testing System DTS-16 was used to apply both dynamic creep and indirect tensile tests that give a good idea about the rutting and fatigue behavior of asphalt mixtures. Both tests were conducted at 25 °C. Results The Zeolitic tuffs modified asphalt mixtures have lower accumulated strains and higher creep stiffness compared to the control mixture. Subsequently, the indirect tensile test results showed that the modified asphalt mixtures have a higher resilient modulus than the control asphalt mix. Generally, using Zeolitic tuffs as a modifier of asphalt mixtures enhanced the rutting and fatigue resistance. The results showed that asphalt mixtures fortified with 25% Zeolitic tuffs emerged as the best contenders for rutting resistance. Also, mixtures enriched with 50% Zeolitic tuffs stood out in fatigue resistance performance. Conclusion The best dynamic creep performance was by adding 25% Zeolitic tuffs by the mineral filler mass, while the best resilient modulus was by adding 50% Zeolitic tuffs by the mineral filler mass. Other The overarching goals encompass understanding the dynamic creep behavior of Zeolitic tuff-infused asphalt blends, deciphering its indirect tensile performance, pinpointing the ideal replacement ratio for mineral fillers, and drawing a performance comparison between WMA and conventional Hot Mix Asphalt (HMA) compositions.

A laboratory investigation was conducted to evaluate the effect of both cross-linked and linear styrene–butadiene–styrene (SBS) modifiers on the cracking resistance of hot mix asphalt (HMA) mixtures. Five types of asphalt mixtures composed by the same aggregate gradation but different asphalt binders were produced in the laboratory. The cracking performances of the mixtures were evaluated using a viscoelastic fracture mechanics-based model entitled ‘HMA Fracture Mechanics’. Crack localisation and crack growth were investigated performing the indirect tensile test and the semi-circular bending test. A digital image correlation system capable of providing full-field strain maps was applied. The results show the benefit of SBS modifiers to the mixture's cracking resistance in terms of reduced rate of damage accumulation and increased tensile limits to failure. Finally, significant damage and first fracture have shown to be strongly more localised in modified specimens than in the unmodified one.