Identification of Materials from Construction Site Images Using Content Based Image Retrieval Techniques

The capability to automatically identify shapes, objects and materials from the image content through direct and indirect methodologies has enabled the development of several civil engineering related applications that assist in the design, construction and maintenance of construction projects. Examples include surface cracks detection, assessment of fire-damaged mortar, fatigue evaluation of asphalt mixes, aggregate shape measurements, velocimentry, vehicles detection, pore size distribution in geotextiles, damage detection and others. This capability is a product of the technological breakthroughs in the area of Image and Video Processing that has allowed for the development of a large number of digital imaging applications in all industries ranging from the well established medical diagnostic tools (magnetic resonance imaging, spectroscopy and nuclear medical imaging) to image searching mechanisms (image matching, content based image retrieval). Content based image retrieval techniques can also assist in the automated recognition of materials in construction site images and thus enable the development of reliable methods for image classification and retrieval. The amount of original imaging information produced yearly in the construction industry during the last decade has experienced a tremendous growth. Digital cameras and image databases are gradually replacing traditional photography while owners demand complete site photograph logs and engineers store thousands of images for each project to use in a number of construction management tasks. However, construction companies tend to store images without following any standardized indexing protocols, thus making the manual searching and retrieval a tedious and time-consuming effort. Alternatively, material and object identification techniques can be used for the development of automated, content based, construction site image retrieval methodology. These methods can utilize automatic material or object based indexing to remove the user from the time-consuming and tedious manual classification process. In this paper, a novel material identification methodology is presented. This method utilizes content based image retrieval concepts to match known material samples with material clusters within the image content. The results demonstrate the suitability of this methodology for construction site image retrieval purposes and reveal the capability of existing image processing technologies to accurately identify a wealth of materials from construction site images.

Embodied energy and gas emission of geotechnical infrastructure

Construction of geotechnical structures produces various environmental impacts. These include depletion of limited natural resources, generation of wastes and harmful substances during material productions and construction, ineffective usage of energy during processing of raw materials into construction materials, and emissions of unwanted gasses during transportation of materials and usage of equipments. With increasing interests in sustainability at the global scale, there is a need to develop a methodology that can assess environmental impacts at such scale for geotechnical construction. Using embodied energy and gas emission, quantitative measures of environmental impact are evaluated using a case study of a new high speed railway line construction in the UK. Based on the results, the keys to energy savings are (a) to optimise the usage of materials with high embodied energy intensity value (b) to optimise the transportation network and logistics for processes using primarily low embodied energy intensity materials and (c) to reuse as much materials on-site as possible to minimise the quantity of spoils or distance to disposal sites. The evaluated embodied energy and embodied carbon values are compared to those of other types of structures and of other activities and carbon tax values. Such comparisons can be used to discuss among various interested parties (clients, contractors, consultants, policy makers, etc) to make the construction industry more energy efficient. © Springer Science+Business Media B.V. 2011.

Coarse-to-fine vision-based localization by indexing scale-invariant features

This paper presents a novel coarse-to-fine global localization approach inspired by object recognition and text retrieval techniques. Harris-Laplace interest points characterized by scale-invariant transformation feature descriptors are used as natural landmarks. They are indexed into two databases: a location vector space model (LVSM) and a location database. The localization process consists of two stages: coarse localization and fine localization. Coarse localization from the LVSM is fast, but not accurate enough, whereas localization from the location database using a voting algorithm is relatively slow, but more accurate. The integration of coarse and fine stages makes fast and reliable localization possible. If necessary, the localization result can be verified by epipolar geometry between the representative view in the database and the view to be localized. In addition, the localization system recovers the position of the camera by essential matrix decomposition. The localization system has been tested in indoor and outdoor environments. The results show that our approach is efficient and reliable. © 2006 IEEE.

The use of syntax and multiple alternatives in the VODIS voice operated database inquiry system

The paper describes the architecture of VODIS, a voice operated database inquiry system, and presents some experiments which investigate the effects on performance of varying the level of a priori syntactic constraints. The VODIS system includes a novel mechanism for incorporating context-free grammatical constraints directly into the word recognition algorithm. This allows the degree of a priori constraint to be smoothly varied and provides for the controlled generation of multiple alternatives. The results show that when the spoken input deviates from the predefined task grammar, a combination of weak a priori syntax rules in conjunction with full a posteriori parsing on a lattice of alternative word matches provides the most robust recognition performance. © 1991.