A robust boundary-based object recognition in occlusion environment by hybrid Hopfield neural networks

Jung Hyoun Kim, Sung Ho Yoon, Kwanghoon H. Sohn

Research output: Contribution to journalArticlepeer-review

26 Citations (Scopus)

Abstract

This paper presents a new method of occluded object matching for machine vision applications. The current methods for occluded object matching lack robustness and require high computational effort. In this paper, a new Hybrid Hopfield Neural Network (HHN) algorithm, which combines the advantages of both a Continuous Hopfield Network (CHN) and a Discrete Hopfield Network (DHN), will be described and applied for partially occluded object recognition in a multi-context scenery. The HHN proposed as a new approach provides great fault tolerance and robustness and requires less computation time. Also, advantages of HHN such as reliability and speed will be discussed.

Original languageEnglish
Pages (from-to)2047-2060
Number of pages14
JournalPattern Recognition
Volume29
Issue number12
DOIs
Publication statusPublished - 1996 Dec

Bibliographical note

Funding Information:
Acknowledgements--This research has been supported by FAA under Grant No. 93-G-012, ARO under Grant No. DAAL03-90-0913, NASA-CORE under Grant No. NAGW-2924, and ARPA under Grant No. N00600-93-K-2051.

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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