Automatic Structural Scene Digitalization

In this paper, we present an automatic system for the analysis and labeling of structural scenes, floor plan drawings in Computer-aided Design (CAD) format. The proposed system applies a fusion strategy to detect and recognize various components of CAD floor plans, such as walls, doors, windows and other ambiguous assets. Technically, a general rule-based filter parsing method is fist adopted to extract effective information from the original floor plan. Then, an image-processing based recovery method is employed to correct information extracted in the first step. Our proposed method is fully automatic and real-time. Such analysis system provides high accuracy and is also evaluated on a public website that, on average, archives more than ten thousands effective uses per day and reaches a relatively high satisfaction rate.Comment: paper submitted to PloS On

Perception of Motion and Architectural Form: Computational Relationships between Optical Flow and Perspective

Perceptual geometry refers to the interdisciplinary research whose objectives focuses on study of geometry from the perspective of visual perception, and in turn, applies such geometric findings to the ecological study of vision. Perceptual geometry attempts to answer fundamental questions in perception of form and representation of space through synthesis of cognitive and biological theories of visual perception with geometric theories of the physical world. Perception of form, space and motion are among fundamental problems in vision science. In cognitive and computational models of human perception, the theories for modeling motion are treated separately from models for perception of form.Comment: 10 pages, 13 figures, submitted and accepted in DoCEIS'2012 Conference: http://www.uninova.pt/doceis/doceis12/home/home.ph

Box Drawings for Learning with Imbalanced Data

The vast majority of real world classification problems are imbalanced, meaning there are far fewer data from the class of interest (the positive class) than from other classes. We propose two machine learning algorithms to handle highly imbalanced classification problems. The classifiers constructed by both methods are created as unions of parallel axis rectangles around the positive examples, and thus have the benefit of being interpretable. The first algorithm uses mixed integer programming to optimize a weighted balance between positive and negative class accuracies. Regularization is introduced to improve generalization performance. The second method uses an approximation in order to assist with scalability. Specifically, it follows a \textit{characterize then discriminate} approach, where the positive class is characterized first by boxes, and then each box boundary becomes a separate discriminative classifier. This method has the computational advantages that it can be easily parallelized, and considers only the relevant regions of feature space

Review and critical analysis: Rolling-element bearings for system life and reliability

A ball and cylindrical roller bearing technical specification which incorporates the latest state-of-the-art advancements was prepared for the purpose of improving bearing reliability in U.S. Army aircraft. The current U.S. Army aviation bearing designs and applications, including life analyses, were analyzed. A bearing restoration and refurbishment specification was prepared to improve bearing availability

Models of Dynamic Data for Emergency Response: A Comparative Study

The first hours after a disaster happens are very chaotic and difficult but perhaps the most important for successfully fighting the consequences, saving human lives and reducing damages in private and public properties. Despite some advances, complete inventory of the information needed during the emergency response remains challenging. In the last years several nationally and internationally funded projects have concentrated on inventory of emergency response processes, structures for storing dynamic information and standards and services for accessing needed data sets. A good inventory would clarify many aspects of the information exchange such as data sets, models, representations; a good structuring would facilitate the fast access to a desired piece of information, as well as the automation of analysis of the information. Consequently the information can be used better in the decision-making process.\ud This paper presents our work on models for dynamic data for different disasters and incidents in Europe. The Dutch data models are derived from a thorough study on emergency response procedure in the Netherlands. Two more models developed within the project HUMBOLDT reflect several cross border disaster management scenarios in Europe. These models are compared with the Geospatial Data Model of the Department of Homeland Security in USA. The paper draws conclusions about the type of geographical information needed to perform emergency response operations and the possibility to have a generic model to be used world-wide

Component Segmentation of Engineering Drawings Using Graph Convolutional Networks

We present a data-driven framework to automate the vectorization and machine interpretation of 2D engineering part drawings. In industrial settings, most manufacturing engineers still rely on manual reads to identify the topological and manufacturing requirements from drawings submitted by designers. The interpretation process is laborious and time-consuming, which severely inhibits the efficiency of part quotation and manufacturing tasks. While recent advances in image-based computer vision methods have demonstrated great potential in interpreting natural images through semantic segmentation approaches, the application of such methods in parsing engineering technical drawings into semantically accurate components remains a significant challenge. The severe pixel sparsity in engineering drawings also restricts the effective featurization of image-based data-driven methods. To overcome these challenges, we propose a deep learning based framework that predicts the semantic type of each vectorized component. Taking a raster image as input, we vectorize all components through thinning, stroke tracing, and cubic bezier fitting. Then a graph of such components is generated based on the connectivity between the components. Finally, a graph convolutional neural network is trained on this graph data to identify the semantic type of each component. We test our framework in the context of semantic segmentation of text, dimension and, contour components in engineering drawings. Results show that our method yields the best performance compared to recent image, and graph-based segmentation methods.Comment: Preprint accepted to Computers in Industr

Explicit and persistent knowledge in engineering drawing analysis

technical reportDomain knowledge permeates all aspects of the engineering drawing analysis process, including understanding the physical processes operating on the medium (i.e., paper), the image analysis techniques, and the interpretation semantics of the structural layout and contents of the drawing. Additionally, an understanding of the broader reverse engineering context, within which the drawing analysis takes place, should be exploited. Thus as part of a wider project on the reverse engineering of legacy systems, we have developed an agent-based engineering analysis system called NDAS (nonDeterministic Agent System). In this paper, we discuss the nature of such a system and how knowledge can be made explicit (both for agents and humans) and how performance models can be de?ned, calibrated, monitored, and improved over time through the use of persistent knowledge. A framework is proposed that allows computational agents to: (1) explore the threshold space for an optimal analysis of the drawing, (2) control information gain through agent invocation, (3) incorporate and communicate knowledge, and (4) inform the software engineering and system development with deep knowledge of the relationships between modules and their parameters (at least in a statistical sense)