Segmentation of color images of plants with a Markovian Mean Shift

Abstract : The segmentation of digital images of plants is a tricky operation. In the example of a plant image on a unhomogeneous background, i.e. taken in its environment, the colorimetric diversity of the elements of a scene or the large number of forms can amplify the phenomena of over-segmentation. Global segmentation methods such as Mean Shift are then in this case the ones which will give the best results. These methods take into account the totality of the pixels of an image before classifying a point. On the other hand, complexity is increased, because it is necessary to go through the whole image treated, in order to find the mode of the point which one wishes to classify. In this article, we plan to couple the global segmentation with a local method which would take over in the event of obvious classification of a given point. The Mean Shift method is used for this purpose in association with Markov's chains.
Liste complète des métadonnées
Contributor : Jimmy Nagau <>
Submitted on : Monday, October 31, 2011 - 2:15:06 AM
Last modification on : Wednesday, July 18, 2018 - 8:11:27 PM




Jimmy Nagau, Jean-Luc Henry. Segmentation of color images of plants with a Markovian Mean Shift. the IEEE Applied Imagery Pattern Recognition Workshop, Oct 2011, Washington DC, United States. pp.1-5, ⟨10.1109/AIPR.2011.6176338⟩. ⟨hal-00637143⟩



Record views