Automated Detection of Concrete Columns from Visual Data

After earthquakes, licensed inspectors use the established codes to assess the impact of damage on structural elements. It always takes them days to weeks. However, emergency responders (e.g. firefighters) must act within hours of a disaster event to enter damaged structures to save lives, and therefore cannot wait till an official assessment completes. This is a risk that firefighters have to take. Although Search and Rescue Organizations offer training seminars to familiarize firefighters with structural damage assessment, its effectiveness is hard to guarantee when firefighters perform life rescue and damage assessment operations together. Also, the training is not available to every firefighter. The authors therefore proposed a novel framework that can provide firefighters with a quick but crude assessment of damaged buildings through evaluating the visible damage on their critical structural elements (i.e. concrete columns in the study). This paper presents the first step of the framework. It aims to automate the detection of concrete columns from visual data. To achieve this, the typical shape of columns (long vertical lines) is recognized using edge detection and the Hough transform. The bounding rectangle for each pair of long vertical lines is then formed. When the resulting rectangle resembles a column and the material contained in the region of two long vertical lines is recognized as concrete, the region is marked as a concrete column surface. Real video/image data are used to test the method. The preliminary results indicate that concrete columns can be detected when they are not distant and have at least one surface visible.

Material parameter extraction in THz-TDS using a converging beam transfer function

We demonstrate a parameter extraction algorithm based on a theoretical transfer function, which takes into account a converging THz beam. Using this, we successfully extract material parameters from data obtained for a quartz sample with a THz time domain spectrometer. © 2010 IEEE.

Material properties and tribological performance of rf-PECVD deposited DLC coatings

Diamond-like carbon (DLC) coatings were deposited on to silicon, glass and metal substrates, using an rf-plasma enhanced chemical vapour deposition (rf-PECVD) process. The resultant film properties were evaluated in respect of material and interfacial property control, based on bias voltage variation and the introduction of inert (He and Ar) and reactive (N2) diluting gases in a CH4 plasma. The analysis techniques used to assess the material properties of the films included AFM, EELS, RBS/ERDA, spectroscopic, electrical, stress, microhardness, and adhesion. These were correlated to the tribological performance of the coatings using wear measurements. The most important observation is that He dilution (>90%) promotes enhanced adhesion with respect to all substrate material studies. Coatings typically exhibit a microhardness of the order of 10-20 GPa in films 0.1material and interfacial property control, based on bias voltage variation and the introduction of inert (He and Ar) and reactive (N2) diluting gases in a CH4 plasma. The analysis techniques used to assess the material properties of the films included AFM, EELS, RBS/ERDA, spectroscopic, electrical, stress, microhardness, and adhesion. These were correlated to the tribological performance of the coatings using wear measurements. The most important observation is that He dilution (>90%) promotes enhanced adhesion with respect to all substrate materials studied. Coatings typically exhibit a microhardness of the order of 10-20 GPa in films 0.1 < d < 2 μm thick, with associated electrical resistivity in the range 108 < ρ < 1012 Ω·cm, coefficient of friction <0.1 and surface RMS roughness as low as 2 A. The results are discussed with respect to surface pre-treatment, ion surface bombardment, interfacial reactivity and changes in plasma gas breakdown processes.

Vision-based global localization using a visual vocabular

This paper presents a novel coarse-to-fine global localization approach that is inspired by object recognition and text retrieval techniques. Harris-Laplace interest points characterized by SIFT descriptors are used as natural land-marks. These descriptors are indexed into two databases: an inverted index and a location database. The inverted index is built based on a visual vocabulary learned from the feature descriptors. In the location database, each location is directly represented by a set of scale invariant descriptors. The localization process consists of two stages: coarse localization and fine localization. Coarse localization from the inverted index is fast but not accurate enough; whereas localization from the location database using voting algorithm is relatively slow but more accurate. The combination of coarse and fine stages makes fast and reliable localization possible. In addition, if necessary, the localization result can be verified by epipolar geometry between the representative view in database and the view to be localized. Experimental results show that our approach is efficient and reliable. ©2005 IEEE.