Combined thresholding and neural network approach for vein pattern extraction from leaf images

H. Fu, Zheru Chi

Research output: Journal article publicationJournal articleAcademic researchpeer-review

61 Citations (Scopus)

Abstract

Living plant recognition based on images of leaf, flower and fruit is a very challenging task in the field of pattern recognition and computer vision. There has been little work reported on flower and fruit image processing and recognition. In recent years, several researchers have dedicated their work to leaf characterisation. As an inherent trait, leaf vein definitely contains the important information for plant species recognition despite its complex modality. A new approach that combines a thresholding method and an artificial neural network (ANN) classifier is proposed to extract leaf veins. A preliminary segmentation based on the intensity histogram of leaf images is first carried out to coarsely determine vein regions. This is followed by a fine segmentation using a trained ANN classifier with ten features extracted from a window centred on the object pixel as its inputs. Compared with other methods, experimental results show that this combined approach is capable of extracting more accurate venation modality of the leaf for the subsequent vein pattern classification. The approach can also reduce the computing time compared with a direct neural network approach.
Original languageEnglish
Pages (from-to)881-892
Number of pages12
JournalIEE Proceedings: Vision, Image and Signal Processing
Volume153
Issue number6
DOIs
Publication statusPublished - 1 Jan 2006

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

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