Parameter optimization for automated concrete detection in image data

Several research studies have been recently initiated to investigate the use of construction site images for automated infrastructure inspection, progress monitoring, etc. In these studies, it is always necessary to extract material regions (concrete or steel) from the images. Existing methods made use of material's special color/texture ranges for material information retrieval, but they do not sufficiently discuss how to find these appropriate color/texture ranges. As a result, users have to define appropriate ones by themselves, which is difficult for those who do not have enough image processing background. This paper presents a novel method of identifying concrete material regions using machine learning techniques. Under the method, each construction site image is first divided into regions through image segmentation. Then, the visual features of each region are calculated and classified with a pre-trained classifier. The output value determines whether the region is composed of concrete or not. The method was implemented using C++ and tested over hundreds of construction site images. The results were compared with the manual classification ones to indicate the method's validity.

Management and Analysis of Unstructured Construction Data Types

Compared with structured data sources that are usually stored and analyzed in spreadsheets, relational databases, and single data tables, unstructured construction data sources such as text documents, site images, web pages, and project schedules have been less intensively studied due to additional challenges in data preparation, representation, and analysis. In this paper, our vision for data management and mining addressing such challenges are presented, together with related research results from previous work, as well as our recent developments of data mining on text-based, web-based, image-based, and network-based construction databases.

Data Analysis on Complicated Construction Data Sources: Vision, Research, and Recent Developments

Compared with construction data sources that are usually stored and analyzed in spreadsheets and single data tables, data sources with more complicated structures, such as text documents, site images, web pages, and project schedules have been less intensively studied due to additional challenges in data preparation, representation, and analysis. In this paper, our definition and vision for advanced data analysis addressing such challenges are presented, together with related research results from previous work, as well as our recent developments of data analysis on text-based, image-based, web-based, and network-based construction sources. It is shown in this paper that particular data preparation, representation, and analysis operations should be identified, and integrated with careful problem investigations and scientific validation measures in order to provide general frameworks in support of information search and knowledge discovery from such information-abundant data sources.

Occlusive Components Analysis

We study unsupervised learning in a probabilistic generative model for occlusion. The model uses two types of latent variables: one indicates which objects are present in the image, and the other how they are ordered in depth. This depth order then determines how the positions and appearances of the objects present, specified in the model parameters, combine to form the image. We show that the object parameters can be learnt from an unlabelled set of images in which objects occlude one another. Exact maximum-likelihood learning is intractable. However, we show that tractable approximations to Expectation Maximization (EM) can be found if the training images each contain only a small number of objects on average. In numerical experiments it is shown that these approximations recover the correct set of object parameters. Experiments on a novel version of the bars test using colored bars, and experiments on more realistic data, show that the algorithm performs well in extracting the generating causes. Experiments based on the standard bars benchmark test for object learning show that the algorithm performs well in comparison to other recent component extraction approaches. The model and the learning algorithm thus connect research on occlusion with the research field of multiple-causes component extraction methods.

How to achieve ultra high photoconductive gain for transparent oxide semiconductor image sensors

We present quantitative analysis of the ultra-high photoconductivity in amorphous oxide semiconductor (AOS) thin film transistors (TFTs), taking into account the sub-gap optical absorption in oxygen deficiency defects. We analyze the basis of photoconductivity in AOSs, explained in terms of the extended electron lifetime due to retarded recombination as a result of hole localization. Also, photoconductive gain in AOS photo-TFTs can be maximized by reducing the transit time associated with short channel lengths, making device scaling favourable for high sensitivity operation. © 2012 IEEE.

Systematic misregistration and the statistical analysis of surface data.

Spatial normalisation is a key element of statistical parametric mapping and related techniques for analysing cohort statistics on voxel arrays and surfaces. The normalisation process involves aligning each individual specimen to a template using some sort of registration algorithm. Any misregistration will result in data being mapped onto the template at the wrong location. At best, this will introduce spatial imprecision into the subsequent statistical analysis. At worst, when the misregistration varies systematically with a covariate of interest, it may lead to false statistical inference. Since misregistration generally depends on the specimen's shape, we investigate here the effect of allowing for shape as a confound in the statistical analysis, with shape represented by the dominant modes of variation observed in the cohort. In a series of experiments on synthetic surface data, we demonstrate how allowing for shape can reveal true effects that were previously masked by systematic misregistration, and also guard against misinterpreting systematic misregistration as a true effect. We introduce some heuristics for disentangling misregistration effects from true effects, and demonstrate the approach's practical utility in a case study of the cortical bone distribution in 268 human femurs.

Rapid patterning of 1-D collagenous topography as an ECM protein fibril platform for image cytometry.

Cellular behavior is strongly influenced by the architecture and pattern of its interfacing extracellular matrix (ECM). For an artificial culture system which could eventually benefit the translation of scientific findings into therapeutic development, the system should capture the key characteristics of a physiological microenvironment. At the same time, it should also enable standardized, high throughput data acquisition. Since an ECM is composed of different fibrous proteins, studying cellular interaction with individual fibrils will be of physiological relevance. In this study, we employ near-field electrospinning to create ordered patterns of collagenous fibrils of gelatin, based on an acetic acid and ethyl acetate aqueous co-solvent system. Tunable conformations of micro-fibrils were directly deposited onto soft polymeric substrates in a single step. We observe that global topographical features of straight lines, beads-on-strings, and curls are dictated by solution conductivity; whereas the finer details such as the fiber cross-sectional profile are tuned by solution viscosity. Using these fibril constructs as cellular assays, we study EA.hy926 endothelial cells' response to ROCK inhibition, because of ROCK's key role in the regulation of cell shape. The fibril array was shown to modulate the cellular morphology towards a pre-capillary cord-like phenotype, which was otherwise not observed on a flat 2-D substrate. Further facilitated by quantitative analysis of morphological parameters, the fibril platform also provides better dissection in the cells' response to a H1152 ROCK inhibitor. In conclusion, the near-field electrospun fibril constructs provide a more physiologically-relevant platform compared to a featureless 2-D surface, and simultaneously permit statistical single-cell image cytometry using conventional microscopy systems. The patterning approach described here is also expected to form the basics for depositing other protein fibrils, seen among potential applications as culture platforms for drug screening.