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

Spatio-structural Symbol Description with Statistical Feature Add-on

The original publication is available at www.springerlink.comInternational audienceIn this paper, we present a method for symbol description based on both spatio-structural and statistical features computed on elementary visual parts, called 'vocabulary'. This extracted vocabulary is grouped by type (e.g., circle, corner ) and serves as a basis for an attributed relational graph where spatial relational descriptors formalise the links between the vertices, formed by these types, labelled with global shape descriptors. The obtained attributed relational graph description has interesting properties that allows it to be used efficiently for recognising structure and by comparing its attribute signatures. The method is experimentally validated in the context of electrical symbol recognition from wiring diagrams

Relation Bag-of-Features for Symbol Retrieval

International audienceIn this paper, we address a new scheme for symbol retrieval based on relation bag-of-features (BOFs) which are computed between the extracted visual primitives. Our feature consists of pairwise spatial relations from all possible combina tions of individual visual primitives. The key characteristic of the overall process is to use topological information to guide directional relations. Consequently, directional relation matching takes place only with those candidates having similar topological configurations. A comprehensive study is made by using two different datasets. Experimental tests provide interesting results by establishing user-friendly symbol retrieval application

BoR: Bag-of-Relations for Symbol Retrieval

International audienceIn this paper, we address a new scheme for symbol retrieval based on bag-of-relations (BoRs) which are computed between extracted visual primitives (e.g. circle and corner). Our features consist of pairwise spatial relations from all possible combinations of individual visual primitives. The key characteristic of the overall process is to use topological relation information indexed in bags-of-relations and use this for recognition. As a consequence, directional relation matching takes place only with those candidates having similar topological configurations. A comprehensive study is made by using several different well known datasets such as GREC, FRESH and SESYD, and includes a comparison with state-of-the-art descriptors. Experiments provide interesting results on symbol spotting and other user-friendly symbol retrieval applications

A computer graphics program for general finite element analyses

Documentation for a computer graphics program for displays from general finite element analyses is presented. A general description of display options and detailed user instructions are given. Several plots made in structural, thermal and fluid finite element analyses are included to illustrate program options. Sample data files are given to illustrate use of the program

Spatio-structural Symbol Description with Statistical Feature Add-on

International audienceIn this paper, we present a method for symbol description based on spatio-structural as well as statistical features of visual elementary parts called 'vocabulary'. The extracted vocabulary is first organised into different groups based on their types (e.g., circle, corner). This vocabulary is used as a basis for an Attributed Relational Graph (ARG) where spatial relational descriptors formalise the links between the types, labelled with global shape descriptors. The description is used to globally recognise structure by comparing the signatures. The method is experimentally validated in the context of electrical symbol recognition from wiring diagrams

Graph matching using position coordinates and local features for image analysis

Encontrar las correspondencias entre dos imágenes es un problema crucial en el campo de la visión por ordenador i el reconocimiento de patrones. Es relevante para un amplio rango de propósitos des de aplicaciones de reconocimiento de objetos en las áreas de biometría, análisis de documentos i análisis de formas hasta aplicaciones relacionadas con la geometría desde múltiples puntos de vista tales cómo la recuperación de la pose, estructura desde el movimiento y localización y mapeo. La mayoría de las técnicas existentes enfocan este problema o bien usando características locales en la imagen o bien usando métodos de registro de conjuntos de puntos (o bien una mezcla de ambos). En las primeras, un conjunto disperso de características es primeramente extraído de las imágenes y luego caracterizado en la forma de vectores descriptores usando evidencias locales de la imagen. Las características son asociadas según la similitud entre sus descriptores. En las segundas, los conjuntos de características son considerados cómo conjuntos de puntos los cuales son asociados usando técnicas de optimización no lineal. Estos son procedimientos iterativos que estiman los parámetros de correspondencia y de alineamiento en pasos alternados. Los grafos son representaciones que contemplan relaciones binarias entre las características. Tener en cuenta relaciones binarias al problema de la correspondencia a menudo lleva al llamado problema del emparejamiento de grafos. Existe cierta cantidad de métodos en la literatura destinados a encontrar soluciones aproximadas a diferentes instancias del problema de emparejamiento de grafos, que en la mayoría de casos es del tipo "NP-hard". El cuerpo de trabajo principal de esta tesis está dedicado a formular ambos problemas de asociación de características de imagen y registro de conjunto de puntos como instancias del problema de emparejamiento de grafos. En todos los casos proponemos algoritmos aproximados para solucionar estos problemas y nos comparamos con un número de métodos existentes pertenecientes a diferentes áreas como eliminadores de "outliers", métodos de registro de conjuntos de puntos y otros métodos de emparejamiento de grafos. Los experimentos muestran que en la mayoría de casos los métodos propuestos superan al resto. En ocasiones los métodos propuestos o bien comparten el mejor rendimiento con algún método competidor o bien obtienen resultados ligeramente peores. En estos casos, los métodos propuestos normalmente presentan tiempos computacionales inferiores.Trobar les correspondències entre dues imatges és un problema crucial en el camp de la visió per ordinador i el reconeixement de patrons. És rellevant per un ampli ventall de propòsits des d’aplicacions de reconeixement d’objectes en les àrees de biometria, anàlisi de documents i anàlisi de formes fins aplicacions relacionades amb geometria des de múltiples punts de vista tals com recuperació de pose, estructura des del moviment i localització i mapeig. La majoria de les tècniques existents enfoquen aquest problema o bé usant característiques locals a la imatge o bé usant mètodes de registre de conjunts de punts (o bé una mescla d’ambdós). En les primeres, un conjunt dispers de característiques és primerament extret de les imatges i després caracteritzat en la forma de vectors descriptors usant evidències locals de la imatge. Les característiques son associades segons la similitud entre els seus descriptors. En les segones, els conjunts de característiques son considerats com conjunts de punts els quals son associats usant tècniques d’optimització no lineal. Aquests son procediments iteratius que estimen els paràmetres de correspondència i d’alineament en passos alternats. Els grafs son representacions que contemplen relacions binaries entre les característiques. Tenir en compte relacions binàries al problema de la correspondència sovint porta a l’anomenat problema de l’emparellament de grafs. Existeix certa quantitat de mètodes a la literatura destinats a trobar solucions aproximades a diferents instàncies del problema d’emparellament de grafs, el qual en la majoria de casos és del tipus “NP-hard”. Una part del nostre treball està dedicat a investigar els beneficis de les mesures de ``bins'' creuats per a la comparació de característiques locals de les imatges. La resta està dedicat a formular ambdós problemes d’associació de característiques d’imatge i registre de conjunt de punts com a instàncies del problema d’emparellament de grafs. En tots els casos proposem algoritmes aproximats per solucionar aquests problemes i ens comparem amb un nombre de mètodes existents pertanyents a diferents àrees com eliminadors d’“outliers”, mètodes de registre de conjunts de punts i altres mètodes d’emparellament de grafs. Els experiments mostren que en la majoria de casos els mètodes proposats superen a la resta. En ocasions els mètodes proposats o bé comparteixen el millor rendiment amb algun mètode competidor o bé obtenen resultats lleugerament pitjors. En aquests casos, els mètodes proposats normalment presenten temps computacionals inferiors

Integrating Vocabulary Clustering with Spatial Relations for Symbol Recognition

International audienceThis paper develops a structural symbol recognition method with integrated statistical features. It applies spatial organization descriptors to the identified shape features within a fixed visual vocabulary that compose a symbol. It builds an attributed relational graph expressing the spatial relations between those visual vocabulary elements. In order to adapt the chosen vocabulary features to multiple and possible specialized contexts, we study the pertinence of unsupervised clustering to capture significant shape variations within a vocabulary class and thus refine the discriminative power of the method. This unsupervised clustering relies on cross-validation between several different cluster indices. The resulting approach is capable of determining part of the pertinent vocabulary and significantly increases recognition results with respect to the state-of-the-art. It is experimentally validated on complex electrical wiring diagram symbols

Analysis of Digital Logic Schematics Using Image Recognition

This thesis presents the results of research in the area of automated recognition of digital logic schematics. The adaptation of a number of existing image processing techniques for use with this kind of image is discussed, and the concept of using sets of tokens to represent the overall drawing i s explained in detail. Methods are given for using tokens to describe schematic component shapes, to represent the connections between components, and to provide sufficient information to a parser so that an equation can be generated. A Microsoft Windows-based test program which runs under Windows 95 or Windows NT has been written to implement the ideas presented. This program accepts either scanned images of digital schematics, or computer-generated images in Microsoft Windows bitmap format as input. It analyzes the input schematic image for content, and produces a corresponding logical equation as output. It also provides the functionality necessary to build and maintain an image token library