Abstract
Automatic plant classification systems are essential for a wide range of applications including environment protection, plant resource survey, as well as for education. With the aid of advanced information technology, image processing and machine learning techniques, automatic plant identification and classification will enhance such systems with more functionality, such as automatic labeling and flexible searching. Image segmentation and object recognition are two aspects of digital image processing which are being increasingly used in many applications including leaf recognition. In this paper, the Preferential Image Segmentation (PIS) method is used to segment an object of interest from the original image. A probabilistic curve evolution method with particle filters is used to measure the similarity between shapes during matching process. The experimental results prove that the preferential image segmentation can be successfully applied in leaf recognition and segmentation from a plant image.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Agarwal, S., Awan, A., Roth, D.: Learning to detect objects in images via a sparse, part-based representation. IEEE Trans. Pattern Anal. Mach. Intell. 26(11), 1475–1490 (2004)
Aujol, J., Aubert, G., Blanc-Féraud, L.: Wavelet-based level set evolution for classification of textured images. IEEE Trans. Image Process. 12(12), 1634–1641 (2003)
Caselles, V., Kimmel, R., Sapiro, G.: Geodesic active contours. Int. J. Comput. Vis. 22(1), 61–79 (1997)
Chan, T.F., Vese, L.A.: Active contours without edges. IEEE Trans. Image Process. 10(2), 266–277 (2001)
Chen, L., Georganas, N.D.: Region-based 3D Mesh Compression Using an Efficient Neighborhood-based Segmentation. Simulation, Society for Computer Simulation International 84(5), 185–195 (2008)
Chen, Y.: Using prior shapes in geometric active contours in a variational framework. Int. J. Comput. Vis. 50(3), 315–328 (2002)
Cremers, D., Funka-Lea, G.: Dynamical statistical shape priors for level set based sequence segmentation. Variational and Level Set Methods Comput. Vis., 210–221 (2005)
Cremers, D., Tischhauser, F., Weickert, J., Schnorr, C.: Diffusion snakes: Introducing statistical shape knowledge into the Mumford–Shah functional. Int. J. Comput. Vis. 50(3), 295–313 (2002)
Farzinfar, M., Xue, Z., Teoh, E.K.: A novel approach for curve evolution in segmentation of medical images. Comput. Med. Imaging Graph 34(5), 354–361 (2010)
Freedman, D., Zhang, T.: Active contours for tracking distributions. IEEE Trans. Image Process. 13(4), 518–526 (2004)
Cotton Incorporated USA (2009), The classification of Cotton, http://www.cottoninc.com/ClassificationofCotton
Kumar, N., Pandey, S., Bhattacharya, A., Ahuja, P.S.: Do leaf surface characteristics affect agrobacterium infection in tea [camellia sinensis (l.) o kuntze]? J. Biosci. 29(3), 309–317 (2004)
National Institute for Agricultural Botany. Chrysanthemum Leaf Classification, Cambridge (2005)
Tzionas, P., Papadakis, S., Manolakis, D.: Plant leaves classification based on morphological features and a fuzzy surface selection technique. In: Fifth International Conference on Technology and Automation, Thessaloniki, Greece, pp. 365–370 (2005)
Wu, S.G., Bao, F.S., Xu, E.Y., Wang, Y., Chang, Y., Xiang, Q.: A Leaf Recognition Algorithm for Plant Classification Using Probabilistic Neural Network. In: IEEE International Symposium on Signal Processing and Information Technology, pp. 11–16 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Valliammal, N., Geethalakshmi, S.N. (2011). Leaf and Flower Recognition Using Preferential Image Segmentation Algorithm. In: Nagamalai, D., Renault, E., Dhanuskodi, M. (eds) Trends in Computer Science, Engineering and Information Technology. CCSEIT 2011. Communications in Computer and Information Science, vol 204. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24043-0_32
Download citation
DOI: https://doi.org/10.1007/978-3-642-24043-0_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24042-3
Online ISBN: 978-3-642-24043-0
eBook Packages: Computer ScienceComputer Science (R0)