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Cabbage/Weed Discrimination with a Region/Contour Based Segmentation Approach for Multispectral Images

Abstract : Image segmentation on outdoor images is a difficult task because it comes up against the complexity and the variability of objects to detect and also of natural phenomena as shadows, highlights, partially overlapping objects. These difficulties conducts us to develop an adapted segmentation tool to this context. Furthermore, accordingly to the development of multispectral sensors, it seems interesting to propose a methodology adapted to multispectral images. Indeed, the segmentation process takes advantage of complementary informations. Our approach consists firstly in carrying out an image over-segmentation y use of a region growing algorithm and secondly, in labeling the obtained regions in c+1 classes with c equal to the number of class object to detect. The c+1 label allows us to distinguish regions having ambiguous properties (undetermined regions). To remove this ambiguity, a collaborative contour/region approach is applied on these undetermined regions. This approach has been tested on a set of cabbage images at different growth levels. In each image, all pixels have been labeled by hand for validation process. The results obtained by this method are compared with those obtained by a direct region growing segmentation.
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-00137914
Contributor : Christophe Fiorio <>
Submitted on : Thursday, March 22, 2007 - 2:59:35 PM
Last modification on : Tuesday, October 13, 2020 - 3:14:25 AM
Long-term archiving on: : Saturday, May 14, 2011 - 3:06:45 AM

Identifiers

  • HAL Id : lirmm-00137914, version 1
  • IRSTEA : PUB00017823

Citation

Nathalie Gorretta, Christophe Fiorio, Gilles Rabatel, John Marchant. Cabbage/Weed Discrimination with a Region/Contour Based Segmentation Approach for Multispectral Images. Information and Technology for Sustainable Fruit and Vegetable Production (FRUTIC), Sep 2005, Montpellier, France. pp.371-380. ⟨lirmm-00137914⟩

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