Object recognition from 2D images using Kohonen self-organized feature maps

Heba Lakany, E.G Schukat-Talamazzini, H Niemann, M.A.R Ghonaimy

Research output: Contribution to journalArticlepeer-review


In this paper, we propose an algorithm for recognition of objects from 2D images. The algorithm is neural network based. It uses Kohonen self-organised feature maps (SOFM) as a vector quantiser whose performance is compared to that of EM technique. A codebook is designed using SOFM and a simple labelling procedure is used to label the feature vectors, then the labelled training data set of di erent objects are recognised by the resilient propagation network. The algorithm is applied to 2D images of industrial objects.
Original languageEnglish
Pages (from-to)301-308
Number of pages8
JournalPattern Recognition and Image Analysis
Issue number3
Publication statusPublished - 1997


  • 2D images
  • Kohonen
  • self organized feature maps


Dive into the research topics of 'Object recognition from 2D images using Kohonen self-organized feature maps'. Together they form a unique fingerprint.

Cite this