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.

Characterisation of carbon nano-onions using Raman spectroscopy

Characteristics of the Raman spectrum from carbon onions have been identified in terms of the position of the G peak and appearance of the transverse optic phonon peaks. Five new peaks were observed in the low wavenumber region, at about 1100, 861, 700, 450 and 250 cm(-1). The origins of these peaks are discussed in terms of the phonon density of states (PDOS) and phonon dispersion curves of graphite. The curvature of the graphene planes is invoked to explain the relaxation of the Raman selection rules and the appearance of the new peaks. The Raman spectrum of carbon onions is compared with that of highly oriented pyrolytic graphite (HOPG). The strain of graphene planes due to curvature has been estimated analytically and is used to account for the downward shift of the G peak. (C) 2003 Elsevier Science B.V. All rights reserved.

Image deconvolution using a Gaussian scale mixtures model to approximate the wavelet sparseness constraint

This paper proposes to use an extended Gaussian Scale Mixtures (GSM) model instead of the conventional ℓ1 norm to approximate the sparseness constraint in the wavelet domain. We combine this new constraint with subband-dependent minimization to formulate an iterative algorithm on two shift-invariant wavelet transforms, the Shannon wavelet transform and dual-tree complex wavelet transform (DTCWT). This extented GSM model introduces spatially varying information into the deconvolution process and thus enables the algorithm to achieve better results with fewer iterations in our experiments. ©2009 IEEE.

Electrical and optical spectroscopy for quantitative screening of hepatic steatosis in donor livers.

Macro-steatosis in deceased donor livers is increasingly prevalent and is associated with poor or non-function of the liver upon reperfusion. Current assessment of the extent of steatosis depends upon the macroscopic assessment of the liver by the surgeon and histological examination, if available. In this paper we demonstrate electrical and optical spectroscopy techniques which quantitatively characterize fatty infiltration in liver tissue. Optical spectroscopy showed a correlation coefficient of 0.85 in humans when referenced to clinical hematoxylin and eosin (H&E) sections in 20 human samples. With further development, an optical probe may provide a comprehensive measure of steatosis across the liver at the time of procurement.