Multi-Modal Image Retrieval from Construction Databases and Model-Based Systems

In the modern and dynamic construction environment it is important to access information in a fast and efficient manner in order to improve the decision making processes for construction managers. This capability is, in most cases, straightforward with today’s technologies for data types with an inherent structure that resides primarily on established database structures like estimating and scheduling software. However, previous research has demonstrated that a significant percentage of construction data is stored in semi-structured or unstructured data formats (text, images, etc.) and that manually locating and identifying such data is a very hard and time-consuming task. This paper focuses on construction site image data and presents a novel image retrieval model that interfaces with established construction data management structures. This model is designed to retrieve images from related objects in project models or construction databases using location, date, and material information (extracted from the image content with pattern recognition techniques).

On sparsity and overcompleteness in image models

Computational models of visual cortex, and in particular those based on sparse coding, have enjoyed much recent attention. Despite this currency, the question of how sparse or how over-complete a sparse representation should be, has gone without principled answer. Here, we use Bayesian model-selection methods to address these questions for a sparse-coding model based on a Student-t prior. Having validated our methods on toy data, we find that natural images are indeed best modelled by extremely sparse distributions; although for the Student-t prior, the associated optimal basis size is only modestly over-complete.

Beyond 2D-grids: A dependence maximization view on image browsing

Ideally, one would like to perform image search using an intuitive and friendly approach. Many existing image search engines, however, present users with sets of images arranged in some default order on the screen, typically the relevance to a query, only. While this certainly has its advantages, arguably, a more flexible and intuitive way would be to sort images into arbitrary structures such as grids, hierarchies, or spheres so that images that are visually or semantically alike are placed together. This paper focuses on designing such a navigation system for image browsers. This is a challenging task because arbitrary layout structure makes it difficult - if not impossible - to compute cross-similarities between images and structure coordinates, the main ingredient of traditional layouting approaches. For this reason, we resort to a recently developed machine learning technique: kernelized sorting. It is a general technique for matching pairs of objects from different domains without requiring cross-domain similarity measures and hence elegantly allows sorting images into arbitrary structures. Moreover, we extend it so that some images can be preselected for instance forming the tip of the hierarchy allowing to subsequently navigate through the search results in the lower levels in an intuitive way. Copyright 2010 ACM.

Time-resolved particle image velocimetry within the nozzle of a drop-on-demand printhead

Time-resolved particle image velocimetry (PIV) has been performed inside the nozzle of a commercially available inkjet print-head to obtain the time-dependent velocity waveform. A printhead with a single transparent nozzle 80 μm in orifice diameter was used to eject single droplets at a speed of 5 m/s. An optical microscope was used with an ultra-high-speed camera to capture the motion of particles suspended in a transparent liquid at the center of the nozzle and above the fluid meniscus at a rate of half a million frames per second. Time-resolved velocity fields were obtained from a fluid layer approximately 200 μm thick within the nozzle for a complete jetting cycle. A Lagrangian finite-element numerical model with experimental measurements as inputs was used to predict the meniscus movement. The model predictions showed good agreement with the experimental results. This work provides the first experimental verification of physical models and numerical simulations of flows within a drop-on-demand nozzle. © 2012 Society for Imaging Science and Technology.

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.

FB1, an E2A fusion partner in childhood leukemia, interacts with U19/EAF2 and inhibits its transcriptional activity

Background: U19/EAF2 is a potential tumor suppressor exhibiting frequent down-regulation and allelic loss in advanced human prostate cancer specimens. U 19/EAF2 has also been identified as ELL-associated factor 2 (EAF2) based on its binding to ELL, a fusion partner of MLL in acute myeloid leukemia. U19/EAF2 is a putative transcription factor with a transactivation domain and capability of sequence-specific DNA binding. Methods: Yeast-two-hybrid-screening was used to identify U19/EAF2-binding partners. Co-immunoprecipitation and mammalian 1-hybrid assay were used to characterize a U19/EAF2-binding partner. Results: FB1, an E2A fusion partner in childhood leukemia, was identified as a binding-partner of U19/EAF2. FB1 also binds to EAF1, the only homologue of U19/EAF2. FB1 also interacts and co-localizes with ELL in the nucleus. Interestingly, FB1 inhibited the transcriptional activity of U19/EAF2 but not EAF1. Conclusions: FB1 is an important binding partner and a functional regulator of U19/EAF2, EAF1, and/or ELL. (c) 2007 Elsevier Ireland Ltd. All rights reserved.