In this paper, we present an attribute graph grammar for image parsing on scenes with man-made objects, such as buildings, hallways, kitchens, and living moms. We choose one class of primitives - 3D planar rectangles projected on images and six graph grammar production rules. Each production rule not only expands a node into its components, but also includes a number of equations that constrain the attributes of a parent node and those of its children. Thus our graph grammar is context sensitive. The grammar rules are used recursively to produce a large number of objects and patterns in images and thus the whole graph grammar is a type of generative model. The inference algorithm integrates bottom-up rectangle detection which activates top-down prediction using the grammar rules. The final results are validated in a Bayesian framework. The output of the inference is a hierarchical parsing graph with objects, surfaces, rectangles, and their spatial relations. In the inference, the acceptance of a grammar rule means recognition of an object, and actions are taken to pass the attributes between a node and its parent through the constraint equations associated with this production rule. When an attribute is passed from a child node to a parent node, it is called bottom-up, and the opposite is called top-down.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Bottom-up/top-down image parsing by attribute graph grammar


    Beteiligte:
    Feng Han, (Autor:in) / Song-Chun Zhu, (Autor:in)


    Erscheinungsdatum :

    01.01.2005


    Format / Umfang :

    793795 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Bottom-up/Top-Down Image Parsing by Attribute Graph Grammar

    Han, F. / Zhu, S.-C. / IEEE | British Library Conference Proceedings | 2005




    Grammar-Constrained Neural Semantic Parsing with LR Parsers

    Baranowski, Artur / Hochgeschwender, Nico | Deutsches Zentrum für Luft- und Raumfahrt (DLR) | 2021

    Freier Zugriff

    GRAPH NEURAL NETWORKS FOR PARSING ROADS

    FERENCZ ANDRAS | Europäisches Patentamt | 2024

    Freier Zugriff