Ship Deck Segmentation in Engineering Document Using Generative Adversarial Networks

Generative adversarial networks (GANs) have become very popular in recent years. GANs have proved to be successful in different computer vision tasks including image-translation, image super-resolution etc. In this paper, we have used GAN models for ship deck segmentation. We have used 2D scanned raster images of ship decks provided by US Navy Military Sealift Command (MSC) to extract necessary information including ship walls, objects etc. Our segmentation results will be helpful to get vector and 3D image of a ship that can be later used for maintenance of the ship. We applied the trained models to engineering documents provided by MSC and obtained very promising results, demonstrating that GANs can be potentially good candidates for this research area

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

Challenges for the Engineering Drawing Lehigh Steel Collection

International audienceThe Lehigh Steel Collection (LSC) is an extremely large, heterogeneous set of documents dating from the 1960's through the 1990's. It was retrieved by Lehigh University after it acquired research facilities from Bethlehem Steel, a now-bankrupt company that was once the second-largest steel producer and the largest shipbuilder in the United States. The documents account for and describe research and development activities that were conducted on site, and consist of a very wide range of technical documentation, handwritten notes and memos, annotated printed documents, etc. This paper addresses only a sub-part of this collection: the approximately 4000 engineering drawings and blueprints that were retrieved. The challenge resides essentially in the fact that these documents come in different sizes and shapes, in a wide variety of conservation and degradation stages, and more importantly in bulk, and without ground-truth. Making them available to the research community through digitization is one step the good direction, the question now is what to do with them. This paper tries to lay down some first basic stepping stones for enhancing the documents' meta-data and annotations

DIGITAL SECURITY: 3D GEOMETRY PROTECTION OF THE AUTOMATICALLY RESTITUTED HISTORICAL BUILDINGS

This paper describes a novel method of data protection of the three-dimensional (3D) models that are obtained from automatic process of geometric restitution, using old two-dimensional (2D) architectural and artistic drawings. The first contribution of our research is the algorithm that includes several image processing steps, which are required in order to define walls, staircases and openings from the digitalized hand drawn architectural plans. The result of this step is detailed 3D model of the digitally processed historical building plans. The experimental confirmation of the algorithm accuracy is 3D model of the Chateau de Versailles, which is descripted by old hand drawings, dating between the end of the XVII and the XIX century. Next part of our research is theoretical and mathematical analysis of geometrical features of such 3D model that is a result of the image processing algorithm. The key-achievement of this part is new method of protecting the geometrical data using optimized adaptive Sparse Quantization Index Modulation (QIM) for embedding data bits into essential structure of the generated model. As a final result we present a secure authentication of the automatically restituted 3D model of the historically important artifact