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Proceedings Paper

Texture analysis with statistical methods for wheat ear extraction
Author(s): M. Bakhouche; F. Cointault; P. Gouton
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Paper Abstract

In agronomic domain, the simplification of crop counting, necessary for yield prediction and agronomic studies, is an important project for technical institutes such as Arvalis. Although the main objective of our global project is to conceive a mobile robot for natural image acquisition directly in a field, Arvalis has proposed us first to detect by image processing the number of wheat ears in images before to count them, which will allow to obtain the first component of the yield. In this paper we compare different texture image segmentation techniques based on feature extraction by first and higher order statistical methods which have been applied on our images. The extracted features are used for unsupervised pixel classification to obtain the different classes in the image. So, the K-means algorithm is implemented before the choice of a threshold to highlight the ears. Three methods have been tested in this feasibility study with very average error of 6%. Although the evaluation of the quality of the detection is visually done, automatic evaluation algorithms are currently implementing. Moreover, other statistical methods of higher order will be implemented in the future jointly with methods based on spatio-frequential transforms and specific filtering.

Paper Details

Date Published: 29 May 2007
PDF: 8 pages
Proc. SPIE 6356, Eighth International Conference on Quality Control by Artificial Vision, 63560L (29 May 2007); doi: 10.1117/12.736913
Show Author Affiliations
M. Bakhouche, ENESAD, Unité Propre GAP (France)
F. Cointault, ENESAD, Unité Propre GAP (France)
P. Gouton, Le2i-CNRS, Univ. of Burgundy (France)

Published in SPIE Proceedings Vol. 6356:
Eighth International Conference on Quality Control by Artificial Vision
David Fofi; Fabrice Meriaudeau, Editor(s)

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