Journal of Pattern Recognition and Intelligent Systems 
Journal of Pattern Recognition and Intelligent Systems(PRIS)
ISSN:2309-0669(Print)       ISSN:2309-0650(Online)
A Model for Selecting Relevant Elements in Plant Characterization The Use of Mathematical Morphology in Extraction of Plant Elements in Natural Images
Full Paper(PDF, 3153KB)
In automatic image analysis, when using natural images, it is important to select the elements in a scene that can lead to correct characterization. Extraneous elements can result in poor values in the parameter evaluation for recognition. Irrelevant elements also increase the processing time, because they can be used incorrectly by the algorithms for characterization or identification. The goal of this paper is to offer identification algorithms and characterizations that will allow effective recognition. We base our procedure on the photographer focus areas to identify those relevant elements in a scene. In this work, we propose a procedure to select the relevant shape from natural images. The treatment uses focal depth with edge detectors. The resulting points are combined with a region partition to obtain relevant shapes.
Keywords:Mathematical Morphology; Edge Detects; Focal Depth; Image Processing; Computer Vision
Author: Jimmy Nagau1, Jean-Luc Henry1
1.Department of Mathematics, Computer sciences, French West indies/LAMIA Laboratory Fouillole Campus, B.P. 592, 97157 pointe-a-Pitre, France
  1. J. Vallade, Structure and development of the plant. Morphogenesis and reproductive biology of angiosperms, 1999.
  3. J. Reid and S. Searcy, “Vision-based guidance of an agricultural tractor,” IEEE Control Systems Magazine, vol. 7(2), pp. 39-43, 1987.
  4. H.T. Sogaard and H.J. Olsen, “Determination of crop rows by image analysis without segmentation,” Computers, vol. 38(2), pp. 141-158, 2003.
  5. T. Bakker, H. Wouters, K. Asselt van, J. Bontsema, L. Tang, J. Muller, and G. Straten van, “A vision based row detection system for sugar beets,” Computers and Electronics in Agriculture, vol. 60(1), pp. 87-95, 2008.
  6. A. Zehm, M. Nobis, and A. Schwabe, “Multiparameter analysis of vertical vegetation structure based on digital image processing,” Flora-Morphology, Distribution, Functional Ecology of plants, vol. 198(2), pp. 142-160, 2003.
  7. A.S. Laliberte, A. Rango, J.E. Herrick, and L. Fredrickson Ed L. Burkett, “An object-based image analysis approach for determining fractional cover of descent and green vegetation with digital plot photography,” Journal of Arid Environments, vol. 69(1), pp. 1-14, 2007.
  8. A. Tellaeche, X.P. Burgos-Artizzu, G. Pajares, and A. Ribeiro, “A vision based method for weeds identification through the bayesian descision theo,” Pattern Recognition, vol. 41(2), pp. 521-530, 2008.
  9. G. Ruiz-Ruiz, J. Gomez, and L.M. Navas-Gracia, “Testing different color spaces based on hue for the environmentally adaptive segmentation algorithm (easa),” Computers and Electronics in Agriculture, vol. 68, pp. 88-96, 2009.
  10. J. Delon, A. Desolneux, J. L. Lisani, and A. Belen Petro, “Color image segmentation using acceptable histogram segmentation,” Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part II, pp. 239-246, 2005.
  11. L. Zheng, J. Zhang, and Q. Wang, “Mean-shift-based color segmentation of images containing green vegetation,” Computers and Electronics in Agriculture, vol. 65, pp. 93-98, 2009.
  12. E. Franz, M.R. Gebhardt, and K.B. Unklesbay, “Shape description if completely visible and partially occluded leaves for identifying plants in digital images,” Transactions of the ASAE, vol. 34(2), pp. 673-681, 1991.
  13. D.E. Guyer, G.E. Miles, L.D. Gaultney, and M.M. Schreiber, “Application of machine vision to shape analysis in leaf and plant identification,” Transactions of the ASAE, vol. 36(1), pp. 163-171, 1993.
  14. T. Katayama, T. Okamato, K. Imou, T. Torri, and T. Mukai, “Identification of plants for wild-flower garden,” Proceedings of the third IFAC/CIGR Workshop on Artificial Intelligence in Agriculture, Elsevier, Amsterdam, pp. 164-169, 1998.
  15. F. Bonn and G. Rochon, “Précis de télédétection: principes et méthodes,” Presse de l’Université du Quebec/AUPELF, vol. 1, pp. 297-420, 1996.
  16. J. Hemming and T. Rath, “Computer-vision-based weed identification under field conditions using controlled lighting,” Journal of Agricultural Engineering Research, vol. 78(3), pp. 233-243, 2001.
  17. S. Di Zenzo, “A note on the gradient of multi-image,” Computer Vision, Graphics, and Image Processing, vol. 33, pp. 116-125, 1986.
  18. D. Comaniciu and P. Meer, “Mean shift: A robust approach toward feature space analysis,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 5, pp. 603-619, 2002.
  19. D. Comaniciu and P. Meer, “Robust analysis of feature spaces: Color image segmentation,” IEEE Conf. Computer Vision and Pattern Recognition (CVPR’97), San Juan, Puerto Rico, pp. 750-755, 1997.
  20. J. Richard, A. Baskurt, K. Idrissi, and G. Lavoué, “Objects of interest-based visual navigation, retrieval, and semantic content identification system,” Computer Vision and Image Understanding, pp. 271-294, 2004.
  21. J. Nagau, A.-S. Capelle-Laizé, C. Fernandez-Maloigne, and J.-L. Henry, “A control operator for perceptual grouping based on the Gestalt vision’s theory,” In Color and Imaging (CIC19), San Jose, CA: États-Unis, 2011.
  22. J. Serra, Image analysis and mathematical morphology: Theoretical advances, Academic Press, London, vol. 1, 1982.