English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  Pattern recognition by labeled graph matching.

von der Malsburg, C. (1988). Pattern recognition by labeled graph matching. Neural Networks, 1(2), 141-148. doi:10.1016/0893-6080(88)90016-0.

Item is

Basic

show hide
Item Permalink: http://hdl.handle.net/11858/00-001M-0000-002D-F3EB-4 Version Permalink: http://hdl.handle.net/11858/00-001M-0000-002D-F3ED-F
Genre: Journal Article

Files

show Files

Locators

show

Creators

show
hide
 Creators:
von der Malsburg, C.1, Author              
Affiliations:
1Department of Molecular Developmental Biology, MPI for biophysical chemistry, Max Planck Society, ou_578590              

Content

show
hide
Free keywords: -
 Abstract: A model for position invariant pattern recognition is presented. Although not demonstrated here, the system is insensitive to distortions. Recognition is based on labeled graph matching. The system consists of two layers of neurons, an input layer, and a memory and recognition layer. The latter consists of subnets to represent individual patterns. In both layers, patterns are represented by labeled graphs. Nodes are “neurons,” labels are local feature types, links are implemented by excitatory connections and represent topology. Recognition is driven by spontaneous dynamic activations of local clusters in the input layer. Network dynamics is able to selectively activate with good reliability corresponding clusters in memory layer. Few cluster activations suffice to identify the subnet and pattern corresponding to the graph in the input layer. The system has been implemented and tested with the help of simulations.

Details

show
hide
Language(s): eng - English
 Dates: 1988
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: Peer
 Identifiers: DOI: 10.1016/0893-6080(88)90016-0
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: Neural Networks
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: -
Pages: - Volume / Issue: 1 (2) Sequence Number: - Start / End Page: 141 - 148 Identifier: -