SEMI-GLOBAL MATCHING WITH SELF-ADJUSTING PENALTIES

The demand for 3D models of various scales and precisions is strong for a wide range of applications, among which cultural heritage recording is particularly important and challenging. In this context, dense image matching is a fundamental task for processes which involve image-based reconstruction of 3D models. Despite the existence of commercial software, the need for complete and accurate results under different conditions, as well as for computational efficiency under a variety of hardware, has kept image-matching algorithms as one of the most active research topics. Semi-global matching (SGM) is among the most popular optimization algorithms due to its accuracy, computational efficiency, and simplicity. A challenging aspect in SGM implementation is the determination of smoothness constraints, i.e. penalties P1, P2 for disparity changes and discontinuities. In fact, penalty adjustment is needed for every particular stereo-pair and cost computation. In this work, a novel formulation of self-adjusting penalties is proposed: SGM penalties can be estimated solely from the statistical properties of the initial disparity space image. The proposed method of self-adjusting penalties (SGM-SAP) is evaluated using typical cost functions on stereo-pairs from the recent Middlebury dataset of interior scenes, as well as from the EPFL Herz-Jesu architectural scenes. Results are competitive against the original SGM estimates. The significant aspects of self-adjusting penalties are: (i) the time-consuming tuning process is avoided; (ii) SGM can be used in image collections with limited number of stereo-pairs; and (iii) no heuristic user intervention is needed

A HOLISTIC APPROACH FOR INSPECTION OF CIVIL INFRASTRUCTURES BASED ON COMPUTER VISION TECHNIQUES

In this work, it is examined the 2D recognition and 3D modelling of concrete tunnel cracks, through visual cues. At the time being, the structural integrity inspection of large-scale infrastructures is mainly performed through visual observations by human inspectors, who identify structural defects, rate them and, then, categorize their severity. The described approach targets at minimum human intervention, for autonomous inspection of civil infrastructures. The shortfalls of existing approaches in crack assessment are being addressed by proposing a novel detection scheme. Although efforts have been made in the field, synergies among proposed techniques are still missing. The holistic approach of this paper exploits the state of the art techniques of pattern recognition and stereo-matching, in order to build accurate 3D crack models. The innovation lies in the hybrid approach for the CNN detector initialization, and the use of the modified census transformation for stereo matching along with a binary fusion of two state-of-the-art optimization schemes. The described approach manages to deal with images of harsh radiometry, along with severe radiometric differences in the stereo pair. The effectiveness of this workflow is evaluated on a real dataset gathered in highway and railway tunnels. What is promising is that the computer vision workflow described in this work can be transferred, with adaptations of course, to other infrastructure such as pipelines, bridges and large industrial facilities that are in the need of continuous state assessment during their operational life cycle

IMPLEMENTING AN ADAPTIVE APPROACH FOR DENSE STEREO-MATCHING

Defining pixel correspondences in stereo-pairs is a fundamental process in automated image-based 3D reconstruction. In this contribution we report on an approach for dense matching, based on local optimization. The approach represents a fusion of state-of-theart algorithms and novel considerations, which mainly involve improvements in the cost computation and aggregation processes. The matching cost which has been implemented here combines the absolute difference of image colour values with a census transformation directly on images gradient of all colour channels. Besides, a new cost volume is computed by aggregating over cross-window support regions with a linearly defined threshold on cross-window expansion. Aggregated costs are, then, refined using a scan-line optimization technique, and the disparity map is estimated using a "winner-takes-all" selection. Occlusions and mismatches are also handled using existing schemes. The proposed algorithm is tested on a standard stereo-matching data-set with promising results. The future tasks mainly include research on refinement of the disparity map and development of a self-adaptive approach for weighting the contribution of different matching cost components

A LOCAL ADAPTIVE APPROACH FOR DENSE STEREO MATCHING IN ARCHITECTURAL SCENE RECONSTRUCTION

In recent years, a demand for 3D models of various scales and precisions has been growing for a wide range of applications; among them, cultural heritage recording is a particularly important and challenging field. We outline an automatic 3D reconstruction pipeline, mainly focusing on dense stereo-matching which relies on a hierarchical, local optimization scheme. Our matching framework consists of a combination of robust cost measures, extracted via an intuitive cost aggregation support area and set within a coarse-tofine strategy. The cost function is formulated by combining three individual costs: a cost computed on an extended census transformation of the images; the absolute difference cost, taking into account information from colour channels; and a cost based on the principal image derivatives. An efficient adaptive method of aggregating matching cost for each pixel is then applied, relying on linearly expanded cross skeleton support regions. Aggregated cost is smoothed via a 3D Gaussian function. Finally, a simple ‘winnertakes-all’ approach extracts the disparity value with minimum cost. This keeps algorithmic complexity and system computational requirements acceptably low for high resolution images (or real-time applications), when compared to complex matching functions of global formulations. The stereo algorithm adopts a hierarchical scheme to accommodate high-resolution images and complex scenes. In a last step, a robust post-processing work-flow is applied to enhance the disparity map and, consequently, the geometric quality o

AUTONOMOUS ROBOTIC INSPECTION IN TUNNELS

In this paper, an automatic robotic inspector for tunnel assessment is presented. The proposed platform is able to autonomously navigate within the civil infrastructures, grab stereo images and process/analyse them, in order to identify defect types. At first, there is the crack detection via deep learning approaches. Then, a detailed 3D model of the cracked area is created, utilizing photogrammetric methods. Finally, a laser profiling of the tunnel’s lining, for a narrow region close to detected crack is performed; allowing for the deduction of potential deformations. The robotic platform consists of an autonomous mobile vehicle; a crane arm, guided by the computer vision-based crack detector, carrying ultrasound sensors, the stereo cameras and the laser scanner. Visual inspection is based on convolutional neural networks, which support the creation of high-level discriminative features for complex non-linear pattern classification. Then, real-time 3D information is accurately calculated and the crack position and orientation is passed to the robotic platform. The entire system has been evaluated in railway and road tunnels, i.e. in Egnatia Highway and London underground infrastructure

Creating a story map using geographic information systems to explore geomorphology and history of Methana peninsula

Story maps are used as an interactive tool for communication and information dissemination. A web-based application using story mapping technology is presented to explore the Methana peninsula. This volcanic area is characterized by specific volcanic geoforms, unique flora and rich history. The story map combines maps, narrative texts and multimedia content. The spatial data produce thematic maps created by a Geographic Information System on geological data, historical monuments, biodiversity and hiking paths. The purpose is to highlight the distinguishing characteristics of the Methana peninsula, to enable users to interact with maps, texts and images and to inform professional and non-professional users about the particular aspects of volcanic areas. © 2018 by the authors