Edge detection from edge knots in live plant image processing

Chenggang Lu, Zheru Chi, Gang Chen, Dan Zhang, Dagan Feng

Research output: Journal article publicationConference articleAcademic researchpeer-review

Abstract

This paper proposes an optimization model for extracting edges in gray-scale images. The model sufficiently utilizes the gray-level information in a pair of orthogonal directions at each considered pixel. The model has three major features in its novelty: (1) Emphasizing the globality of traditional local features; (2) Being a generalized case of the classical snake models; and (3) Offering a theoretical interpretation to the setting of the parameters for the method based on the Simulation of Particle Motion in a Vector Image Field (SPMVIF). Our Edge Detection from Edge Knots (EDEK) model can be divided into two stages: the generation of edge knots on or near edges and a propagation process for producing complete edges from these edge knots. One advantage of our approach is that the propagation process does not depend on any control parameters. The EDEK model is suitable for live plant image processing, which is demonstrated by a number of simulation results on the edge detection of live plant images. Our model is simple in computing and robust, and can perform very well even in situations where high curvature exists.
Original languageEnglish
Pages (from-to)260-267
Number of pages8
JournalProceedings of SPIE - The International Society for Optical Engineering
Volume4875
Issue number1
DOIs
Publication statusPublished - 1 Jan 2002
EventSecond International Conference on Image and Graphics - Hefei, China
Duration: 16 Aug 200218 Aug 2002

Keywords

  • Edge detection
  • Edge knots
  • Plant image processing
  • Snakes
  • SPMVIF method
  • Strong variation
  • Weak variation

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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