Library

X
feed icon rss

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
  • 1
    Electronic Resource
    Electronic Resource
    Explanation facility for neural networks (1992)
    Springer
    Journal of intelligent and robotic systems 5 (1992), S. 193-206 
    Details
    ISSN: 1573-0409
    Keywords: Neural networks ; explanation facilities ; Prolog ; input characterization grammar ; text generation
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Notes: Abstract This paper give a methodology, PROLOG code, as well as an example of an explanation facility EN applicable to most neural networks. It involves How?, Why? and TRACE facilities, and is based on a general explanation degree calculation in a multilayer neural network, as well as on input node characterization grammars for synthesis of explanation text.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00444296
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Paper (German National Licenses)
    Fulltext
  • 2
    Electronic Resource
    Electronic Resource
    PDL-HM: morphological and syntactic shape classification algorithm (1994)
    Springer
    Machine vision and applications 7 (1994), S. 59-68 
    Details
    ISSN: 1432-1769
    Keywords: Industrial machine vision ; Real-time processing ; Image morphology ; Hit- and-miss transform ; Picture description language ; Neural networks ; Shape classification ; Fish processing
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract This paper describes a novel shape classification technique in computer vision called PDL-HM, which combines the selection of morphological structuring elements from contour description languages, the extraction of shape characteristics by the hit- and-miss transform, and shape classification by a neural network. This real-time method is orientation independent and tolerates limited object overlap. It is illustrated by an application to fish species classification in an industrial enviromnent and performs excellently
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF01215802
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Paper (German National Licenses)
    Fulltext
Close ⊗
This website uses cookies and the analysis tool Matomo. More information can be found here...