Innovative Stereo Vision-Based Approach to Generate Dense Depth Map of Transportation Infrastructure

Three-dimensional (3-D) spatial data of a transportation infrastructure contain useful information for civil engineering applications, including as-built documentation, on-site safety enhancements, and progress monitoring. Several techniques have been developed for acquiring 3-D point coordinates of infrastructure, such as laser scanning. Although the method yields accurate results, the high device costs and human effort required render the process infeasible for generic applications in the construction industry. A quick and reliable approach, which is based on the principles of stereo vision, is proposed for generating a depth map of an infrastructure. Initially, two images are captured by two similar stereo cameras at the scene of the infrastructure. A Harris feature detector is used to extract feature points from the first view, and an innovative adaptive window-matching technique is used to compute feature point correspondences in the second view. A robust algorithm computes the nonfeature point correspondences. Thus, the correspondences of all the points in the scene are obtained. After all correspondences have been obtained, the geometric principles of stereo vision are used to generate a dense depth map of the scene. The proposed algorithm has been tested on several data sets, and results illustrate its potential for stereo correspondence and depth map generation.

Automated On-Site Retrieval of Project Information

Among several others, the on-site inspection process is mainly concerned with finding the right design and specifications information needed to inspect each newly constructed segment or element. While inspecting steel erection, for example, inspectors need to locate the right drawings for each member and the corresponding specifications sections that describe the allowable deviations in placement among others. These information seeking tasks are highly monotonous, time consuming and often erroneous, due to the high similarity of drawings and constructed elements and the abundance of information involved which can confuse the inspector. To address this problem, this paper presents the first steps of research that is investigating the requirements of an automated computer vision-based approach to automatically identify “as-built” information and use it to retrieve “as-designed” project information for field construction, inspection, and maintenance tasks. Under this approach, a visual pattern recognition model was developed that aims to allow automatic identification of construction entities and materials visible in the camera’s field of view at a given time and location, and automatic retrieval of relevant design and specifications information.

Material-Based Construction Site Image Retrieval

The technological advancements in digital imaging, the widespread popularity of digital cameras, and the increasing demand by owners and contractors for detailed and complete site photograph logs have triggered an ever-increasing growth in the rate of construction image data collection, with thousands of images being stored for each project. However, the sheer volume of images and the difficulties in accurately and manually indexing them have generated a pressing need for methods that can index and retrieve images with minimal or no user intervention. This paper reports recent developments from research efforts in the indexing and retrieval of construction site images in architecture, engineering, construction, and facilities management image database systems. The limitations and benefits of the existing methodologies will be presented, as well as an explanation of the reasons for the development of a novel image retrieval approach that not only can recognize construction materials within the image content in order to index images, but also can be compatible with existing retrieval methods, enabling enhanced results.

Content-Based Search Engines for Construction Image Databases

Images represent a valuable source of information for the construction industry. Due to technological advancements in digital imaging, the increasing use of digital cameras is leading to an ever-increasing volume of images being stored in construction image databases and thus makes it hard for engineers to retrieve useful information from them. Content-Based Search Engines are tools that utilize the rich image content and apply pattern recognition methods in order to retrieve similar images. In this paper, we illustrate several project management tasks and show how Content-Based Search Engines can facilitate automatic retrieval, and indexing of construction images in image databases.

Mechanisms of projectile penetration in Dyneema® encapsulated aluminum structures

Polymer composites comprising ultra-high molecular weight polyethylene (UHWMPE) fibers in a compliant matrix are now widely used in ballistic applications with varying levels of success. This is primarily due to a poor understanding of the mechanics of penetration of these composites in ballistic protection systems. In this study, we report experimental observations of the penetration mechanisms in four model systems impacted by a 12.7 mm diameter spherical steel projectile. The four model targets designed to highlight different penetration mechanisms in Dyneema® UHWMPE composites were: (i) a bare aluminum plate; (ii) the same plate fully encased in a 5.9 mm thick casing of Dyneema®; (iii) the fully encased plate with a portion of the Dyneema® removed from the front face so that the projectile impacts directly the Al plate; and (iv) the fully encased plate with a portion of the Dyneema® removed from the rear face so that the projectile can exit the Al plate without again interacting with the Dyneema®. A combination of synchronized high speed photography with three cameras, together with post-test examination of the targets via X-ray tomography and optical microscopy was used to elucidate the deformation and perforation mechanisms. The measurements show that the ballistic resistance of these targets increases in the order: bare Al plate, rear face cutout target, fully encased target and front face cutout target. These findings are explained based on the following key findings: (a) the ballistic performance of Dyneema® plates supported on a foundation is inferior to Dyneema® plates supported along their edges; (b) the apparent ballistic resistance of Dyneema® plates increases if the plates are given an initial velocity prior to the impact by the projectile, thereby reducing the relative velocity between the Dyneema® plate and projectile; and (c) when the projectile is fragmented prior to impact, the spatially and temporally distributed loading enhances the ballistic resistance of the Dyneema®. The simple model targets designed here have elucidated mechanisms by which Dyneema® functions in multi-material structures. © 2014 Elsevier Ltd.

SLP: A zero-contact non-invasive method for pulmonary function testing

Structured Light Plethysmography (SLP) is a novel non-invasive method that uses structured light to perform pulmonary function testing that does not require physical contact with a patient. The technique produces an estimate of chest wall volume changes over time. A patient is observed continuously by two cameras and a known pattern of light (i.e. structured light) is projected onto the chest using an off-the-shelf projector. Corner features from the projected light pattern are extracted, tracked and brought into correspondence for both camera views over successive frames. A novel self calibration algorithm recovers the intrinsic and extrinsic camera parameters from these point correspondences. This information is used to reconstruct a surface approximation of the chest wall and several novel ideas for 'cleaning up' the reconstruction are used. The resulting volume and derived statistics (e.g. FVC, FEV) agree very well with data taken with a spirometer. © 2010. The copyright of this document resides with its authors.