Research Article | OPEN ACCESS
Hevea Leaves Boundary Identification based on Morphological Transformation and Edge Detection Features
1Sule Tekkesinoglu, 1Mohd Shafry Mohd Rahim, 2Amjad Rehman, 1Ismail Mat Amin and 3Tanzila Saba
1Faculty of Computing, University Teknologi Malaysia, Skudai, Malaysia
2MIS Department, College of Business Administration, Salman Bin Abdul Aziz University, Alkharj, KSA
3College of Computer and Information Sciences, Prince Sultan University, Riyadh, KSA
Research Journal of Applied Sciences, Engineering and Technology 2014 12:2447-2451
Received: June 11, 2012 | Accepted: July 04, 2013 | Published: March 29, 2014
Abstract
The goal of this study is to present a concept to identify overlapping rubber tree (Hevea brasiliensis-scientific name) leaf boundaries. Basically rubber tree leaves show similarity to each other and they may contain similar information such as color, texture or shape of leaves. In fact rubber tree leaves are naturally in class of palmate leaves, it means that numbers of leaves are joining at their base. So it reflects the information of the position of the leaves whether the leaves are overlapped or separated. Therefore, this unique feature could be used to distinguish particular leaves from others clone to identify the type of trees. This study addresses the problem of identifying the overlapped leaves with complex background. The morphological transformation is often applied in order to obtain the foreground object and the background location as well. However, it does not yield satisfactory results in order to get boundaries information. This study, presents on improved approach to identify boundary of rubber tree leaves based on morphological operation and edge detection methods. The outcome of this fused algorithm exhibits promising results for identifying the leaf boundaries of rubber trees.
Keywords:
Edge detection, image segmentation, morphological transformation, overlapping, rubber tree leaves,
References
-
Bhatia, G. and V. Chahar, 2011. An enhanced approach to improve the contrast of images having bad light by detecting and extracting their background. Int. J. Comput. Sci. Manage. Stud., 11(2): 2231-5268.
-
Canny, J., 1986. A computational approach to edge detection. IEEE T. Pattern Anal., PAMI-8(6): 679-698.
CrossRef
-
Casanova, D., J.J. de Mesquita Sa Junior and O.M. Bruno, 2012. Plant leaf identification using gabor wavelets. Int. J. Imag. Syst. Technol., 9: 236-243.
-
Chin-Hung, T., K. Yi-Ting and C. Yung-Sheng, 2011. Leaf segmentation, classification and three-dimensional recovery from a few images with close viewpoints. Opt. Eng., 50(3): 037003.
CrossRef
-
Guillaume, C., T. Laure, V. Antoine and C. Didier, 2011. A parametric active polygon for leaf segmentation and shape estimation. Proceedings of the 7th International Conference on Advances in Visual Computing-Volume Part I (ISVC'11), 6938: 202-213.
-
Jie-Yun, B. and E.R. Hong, 2011. An algorithm of leaf image segmentation based on color features. Key Eng. Mater., 474-476: 846-851.
CrossRef
-
Jiménez-Sánchez, A.R., J.D. Mendiola-Santiba-ez, I.R. Terol-Villalobos, G. Herrera-Ruíz, D. Vargas-Vázquez, J.J. García-Escalante and A. Lara-Guevara, 2009. Morphological background detection and enhancement of images with poor lighting. IEEE T. Image Process., 18(3): 613-623.
CrossRef PMid:19211334
-
John, T.A., G. Muthupandi and J. Raju, 2011. Background detection of image using approximation by block and opening by reconstruction transformation. Proceedings of the International Conference on Emerging Technology Trends (ICETT), pp: 26-31.
-
Kennedy, N. and S.D. Noble, 2007. Using structured lighting and shadow for leaf segmentation. Paper No. RRV07142, Proceedings of the ASABE/CSBE North Central Intersectional Meeting.
-
Noble, S.D. and R.B. Brown, 2008. Spectral band selection and testing of edge-subtraction leaf segmentation. Can. Biosyst. Eng., 50: 1492-9058.
-
Otsu, N., 1979. A threshold selection method from gray-level histograms. IEEE T. Syst. Man Cyb., 9(1): 62-66.
CrossRef
-
Rehman, A. and T. Saba, 2011. Performance analysis of segmentation approach for cursive handwritten word recognition on benchmark database. Digit. Signal Process., 21: 486-490.
CrossRef
-
Rehman, A. and T. Saba, 2012. Neural network for document image preprocessing. Artif. Intell. Rev., DOI: 10.1007/s10462-012-9337-z.
CrossRef
-
Saba, T. and A. Rehman, 2012. Effects of artificially intelligent tools on pattern recognition. Int. J. Mach. Learn. Cybern., 4: 155-162.
CrossRef
-
Saba, T. and A. Altameem, 2013. Analysis of vision based systems to detect real time goal events in soccer videos. Appl. Artif. Intell., 27(7): 656-667.
CrossRef
-
Saba, T., A. Rehman and G. Sulong, 2010. Non-linear segmentation of touched roman characters based on genetic algorithm. Int. J. Comput. Sci. Eng., 2(6): 2167-2172.
-
Saba, T., A. Rehman and M. Elarbi-Boudihir, 2011. Methods and strategies on off-line cursive touched characters segmentation: A directional review. Artif. Intell. Rev., DOI: 10.1007/s10462-011-9271-5.
CrossRef
-
Saba, T., S. Alzorani and A. Rehman, 2012. Expert system for offline clinical guidance and treatment. Life Sci. J., 9(4): 2639-2658.
-
Serra, J., 1982. Mathematical Morphology. Vol. I. Academic, London, U.K.
-
Sharon, E., A. Brandt and R. Basri, 2001. Segmentation and boundary detection using multiscale intensity measurements. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
CrossRef
-
Shigematsu, A., N. Mizoue, T. Kajisa and S. Yoshida, 2011. Importance of rubberwood in wood export of Malaysia and Thailand. New Forest., 41(2): 179-189.
CrossRef
-
Sulong, G., T. Saba and A. Rehman, 2010. Dynamic programming based hybrid strategy for offline cursive script recognition. Proceeding of the 2nd IEEE International Conference on Computer and Engineering, 2: 580-584.
CrossRef
-
Terol-Villalobos, I.R., 2005. Morphological Image Enhancement and Segmentation with Analysis. In: Hawkes, P.W. (Ed.), Academic, New York, pp: 207-273.
-
Thibaut, B., S.C. James, R. Paolo and B. Sarah, 2011. Shape and texture based plant leaf classification. Digital Imaging Research Centre, Kingston University, London, UK.
-
Valliammal, N. and S.N. Geethalakshmi, 2011. Hybrid image segmentation algorithm for leaf recognition and characterization. Proceeding of the International Conference Process Automation, Control and Computing (PACC).
CrossRef
-
Valliammal, N. and S.N. Geethalakshmi, 2012. Plant leaf segmentation using non linear K means clustering. Int. J. Comput. Sci. Issues, 9(3): 1694-0814.
Competing interests
The authors have no competing interests.
Open Access Policy
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Copyright
The authors have no competing interests.
|
|
|
ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
|
Information |
|
|
|
Sales & Services |
|
|
|