Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30184
Wood Species Recognition System

Authors: Bremananth R, Nithya B, Saipriya R

Abstract:

The proposed system identifies the species of the wood using the textural features present in its barks. Each species of a wood has its own unique patterns in its bark, which enabled the proposed system to identify it accurately. Automatic wood recognition system has not yet been well established mainly due to lack of research in this area and the difficulty in obtaining the wood database. In our work, a wood recognition system has been designed based on pre-processing techniques, feature extraction and by correlating the features of those wood species for their classification. Texture classification is a problem that has been studied and tested using different methods due to its valuable usage in various pattern recognition problems, such as wood recognition, rock classification. The most popular technique used for the textural classification is Gray-level Co-occurrence Matrices (GLCM). The features from the enhanced images are thus extracted using the GLCM is correlated, which determines the classification between the various wood species. The result thus obtained shows a high rate of recognition accuracy proving that the techniques used in suitable to be implemented for commercial purposes.

Keywords: Correlation, Grey Level Co-Occurrence Matrix, ProbabilityDensity Function, Wood Recognition.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1057457

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2092

References:


[1] Marzuki Khalid, Design of an Intelligent Wood Species Recognition System, Center for Artificial Intelligence and Robotics (CAIRO), Malaysia, 2008.
[2] R. Jordan, Classification of Wood Species by Neural Network Analysis of Ultrasonic Signals, Ultrasonic 36(1-5), pp. 219-222, 1998.
[3] J. Lampinen and S. Somlander, Self Organizing Feature Extraction in Recognition of Wood Surface Defects and Color Images, International Journal of Pattern Recognition and Artificial Intelligence, 1996.
[4] H. Kauppinen, Development of a Color Machine Vision Method for Wood Surface Inspection, Ph.D Thesis, Department of Electrical Engineering, University of Oulu, 1999.
[5] Brandtberg, An Wood Identification System, that classify individual tree crown into respective wood species, 2002.
[6] Jing Yi Tou, Yong Haur Tay and Phooi Yee Lau, Gabor Filter and Gray-level Co-Occurrence Matrices In Texture Classification, Computer Vision and Intelligent System (CVIS) Group, Faculty of Information and Communication Technology, University Tunku Abdul Rahman (UTAR), Malaysia, 2002.