Analyzing the Efficiency of Segment Boundary Detection Using Neural Networks

A. V. Kugaevskikh, A. A. Sogreshilin

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


This paper describes the architecture of a neural network for edge detection. Different filters for first-layer neurons are compared. Neural network learning based on a cosine measure algorithm shows much worse results than an error backpropagation algorithm. Optimal parameters for the first-layer neuron operation are given. The proposed architecture fulfills the stated tasks on edge selection.

Original languageEnglish
Pages (from-to)414-422
Number of pages9
JournalOptoelectronics, Instrumentation and Data Processing
Issue number4
Publication statusPublished - 1 Jul 2019


  • cosine measure
  • edge selection
  • Gabor filter
  • hyperbolic tangent
  • Mexican hat wavelet
  • neural networks


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