Proceedings International Workshop on Mobile Mapping Technology

li, rongxing

Bibliographic data

Monograph

Persistent identifier:: 856671290

Author:: Li, Rongxing

Title:: Proceedings International Workshop on Mobile Mapping Technology

Sub title:: April 21 - 23, 1999, Bangkok, Thailand

Scope:: 1 Online-Ressource (Getr. Zählung [ca. 400 Seiten])

Year of publication:: 1999

Place of publication:: London

Publisher of the original:: RICS Books

Identifier (digital):: 856671290

Illustration:: Illustrationen, Diagramme, Karten

Language:: English

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2016

Document type:: Monograph

Collection:: Earth sciences

Chapter

Title:: [Session 7A: Automatic Object Extraction and Recognition]

Document type:: Monograph

Structure type:: Chapter

Chapter

Title:: A MULTILAYER HOPFIELD NEURAL NETWORK FOR 3-D OBJECT RECOGNITION. Zhuowen Tu and Ron Li.

Document type:: Monograph

Structure type:: Chapter

A MULTILAYER HOPFIELD NEURAL NETWORK FOR 3-D OBJECT RECOGNITION Zhuowen Tu and Ron Li Department of Civil and Environmental Engineering and Geodetic Science The Ohio State University USA tu.37@osu.edu KEY WORDS: Hopfield neural network, 3-D object recognition, back-projection, 2-D image ABSTRACT To recognize 3-D objects in aerial images, a two-layer Hopfield neural network that matches extracted edges and regions in images with back-projected edges and regions from object models is applied. The interconnection between the line layer and the region layer gives strong geometric constraints that force the system to converge to a stable situation if initial values of neuron states are appropriately selected. The algorithm is demonstrated by its use in recognition of trucks from aerial images georeferenced by GPS and INS. 1. INTRODUCTION The problem of three dimensional object recognition has been studied for many years. Today, we are still challenged by the question: "How does the human visual system represent three dimensional objects for recognition?" (Bulthoff et al. 1994). Different endeavors to answer this question, however, yield different achievements of 3-D object recognition systems. For example, the viewpoint invariants matching strategy is under the assumption that 3-D object is represented in brain by 3-D invariants such as relative orientation, collinearity, parallelism etc., while viewpoint dependent solution tries to match image features with 2-D features stored in database in terms of different point of views. In Ullman and Basri (1991), all the views of a given rigid object can be represented by linear combinations of several views under an orthographic projection in which extracted 3-D invariants match with those of a model by alignment. In Lin et al. (1991) 3-D geometric invariants, specifically, areas and relational distances, are matched with those of a model using Hopfield neural network. Properties of vanishing points are well used in Shufelt (1996) to recognize buildings in airborne images. More published papers are dealing with 2-D images of 3-D objects because 3-D invariants are quite difficult to obtain in most cases. Clemens (1991) and Jacobs (1992) gave a framework of how indexing and grouping are applied to recognize 3-D objects in 2-D images while keeping searching space small. Interest points in images are matched with those of candidate model over affine transformations in Lamdan et al. (1990). Pontil and Verri (1998) introduced a Support Vector Machine that builds the optimal hype-plane dividing linearly separable samples. Cohen and Wang (1994) and Wang and Cohen (1994) recognized objects from 3-D curves under affine transformation using B- splines that conserve some useful properties like view invariants. The difficulty of recognition of 3-D objects in 2-D images lies in that there are infinite number of 2-D candidates, sometimes occluded or deformed, formed by even a single 3-D object in different views. Hopfield neural networks (Hopfield and Tank 1985) are one of solutions of object recognition as an optimization problem when a minimization of Lyapunov energy is reached. The global minimum can be obtained when initial values are carefully selected. Lin et al. (1991) described a hierarchical approach in which a coarse-to-fine strategy is addressed to match 3-D object. Suganthan et al.(1995a and 1995b) introduced a new way of selecting initial states of neurons and connection matrix together with annealing method. Young et al. (1997) addressed a multilayer Hopfield network through pyramid images. In this paper, extracted edges and regions are matched with edges and regions back-projected from a 3-D model through the known absolute position and attitude of the image derived from GPS and INS data. The algorithm presented here differs from the above methods in that both edges and regions of input features are matched to those of the model simultaneously through the constraints in each layer and the interconnection between them. More details of recognition of 3-D objects from mobile mapping data using Hopfield neural networks is addressed in Li et al. (1998a). In this paper we first introduce the general concept of Hopfield neural networks. A two layer Hopfield neural network incorporating edge layer and region layer is then described. Finally, an experiment of the application of a two layer Hopfield neural network in truck recognition is presented. 2. SINGLE LAYER HOPFIELD NEURAL NETWORK Object recognition by graph matching, also referred to as morphism, is a mapping from a scene graph to a model graph. The morphism can be categorized on the basis of the constraints that are enforced during the mapping as follows: when the mapping is one-to-one and onto, it is an isomorphism; when it is one-to-one, it is a monomorphism; and when it is many-to-one, it is a homomorphism. Figure 1 gives a basic framework of the Hopfield neural network. k Input features Figure 1. Neuron states and candidate model-input features correspondence. 7A-3-1

Access restriction

Copyright

fullscreen: Proceedings International Workshop on Mobile Mapping Technology

Monograph

Chapter

Chapter

Table of contents

Cite and reuse

Monograph

Chapter

Image

Image fragment

Citation links

Citation recommendation