Pattern recognition based on morphological shape analysis and neural networks

P. Yu, V. Anastassopoulos and A.N. Venetsanopoulos

Mathematics and Computers in Simulation (MATCOM), 1996, vol. 40, issue 5, 577-595

Abstract: Morphological transformations are efficient methods for shape analysis and representation. In this paper two morphological shape descriptors are described for object feature representation. Neural networks are then employed for object recognition and classification. Various coding schemes and training procedures are examined in order to achieve a high classification performance. Complete classification and recognition schemes are proposed, which are shown to work satisfactorily even for objects distorted by quantization noise as well as partially invisible objects. Classification results are compared with those obtained by other shape descriptors which use conventional recognition and classification schemes.

Keywords: Pattern recognition; Mathematical morphology; Neural networks (search for similar items in EconPapers)
Date: 1996
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/0378475495000089
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:40:y:1996:i:5:p:577-595

DOI: 10.1016/0378-4754(95)00008-9

Access Statistics for this article

Mathematics and Computers in Simulation (MATCOM) is currently edited by Robert Beauwens

More articles in Mathematics and Computers in Simulation (MATCOM) from Elsevier
Bibliographic data for series maintained by Catherine Liu ().