CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
Reseach Article

Article:Defect Identification of Lumber Through Correlation Technique with Statistical Feature Extraction Method

by Dr. B. Nagarajan, Dr. Amitabh Wahi, R.Athilakshmi
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 4 - Number 9
Year of Publication: 2010
Authors: Dr. B. Nagarajan, Dr. Amitabh Wahi, R.Athilakshmi
10.5120/858-1201

Dr. B. Nagarajan, Dr. Amitabh Wahi, R.Athilakshmi . Article:Defect Identification of Lumber Through Correlation Technique with Statistical Feature Extraction Method. International Journal of Computer Applications. 4, 9 ( August 2010), 4-7. DOI=10.5120/858-1201

@article{ 10.5120/858-1201,
author = { Dr. B. Nagarajan, Dr. Amitabh Wahi, R.Athilakshmi },
title = { Article:Defect Identification of Lumber Through Correlation Technique with Statistical Feature Extraction Method },
journal = { International Journal of Computer Applications },
issue_date = { August 2010 },
volume = { 4 },
number = { 9 },
month = { August },
year = { 2010 },
issn = { 0975-8887 },
pages = { 4-7 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume4/number9/858-1201/ },
doi = { 10.5120/858-1201 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T19:52:36.036148+05:30
%A Dr. B. Nagarajan
%A Dr. Amitabh Wahi
%A R.Athilakshmi
%T Article:Defect Identification of Lumber Through Correlation Technique with Statistical Feature Extraction Method
%J International Journal of Computer Applications
%@ 0975-8887
%V 4
%N 9
%P 4-7
%D 2010
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Feature extraction is an important component of a pattern recognition system. A well-defined feature extraction algorithm makes the identification process more effective and efficient. Quality checking is one of the most prominent steps in many applications using Feature extraction. Several techniques exist for the quality checking of wooden materials. However, image based quality checking of wooden materials still remains a challenging task. Although trivial quality checking methods are available, they do not give useful results in most situations. This paper addresses the issue of quality checking of wooden materials using feature extraction techniques with high accuracy and reliability. Experiments conducted under the proposed conditions showing significant results are presented.

References
  1. Pasquale Fiauto and Salvatore Musella, “Quality Control within an Expert System Prototype Development” Proc. Fourth International Conference on SoftwareEngineering and KnowledgeEngineering , pp. 435 – 442, June 1992. Digital Object Identifier 10.1109/SEKE.1992.227959
  2. Junfeng Li and Wenzhan Dai, “Quality Assessment Based on the Correlationcoefficient and the 2-D Discrete Wavelet Transform” Proc. IEEE International Conference on Automation and Logistics, pp. 789 – 793,Aug 2009. Digital Object Identifier 10.1109/ICAL.2009.5262815
  3. Christof Knoess, Tim Gremillion, Matthias Schmand, Mike E. Casey, Lars Eriksson, Mark Lenox, Jon T. Treffert,Stefan Vollmar, Guenter Fluegge, Klaus Wienhard, Wolf-Dieter Heiss, and Ron Nutt, “ Development of a Daily Quality Check Procedure for the High- Resolution Research Tomograph (HRRT) Using Natural LSO Background Radioactivity” Proc. IEEE Transactions on vol. 36, Issue 1, pp. 194 – 197,Feb 2006. Digital Object Identifier 10.1109/SEKE.1992.227959
  4. Liwei Wang, Xiao Wang and Jufu Feng, “Correspondence On Image Matrix Based Feature Extraction Algorithms,” Proc. IEEE Transactions on Systems, Man, and Cybernetics, Part B vol. 39, Issue 3, pp.521 – 528,Mar 2001. Digital Object Identifier 10.1109/TSMCB.2005.852471
  5. Chulhee Lee and Euisun Choi, “Optimizing Feature Extraction for Multiclass Problems,”Proc. 15th International Conference on Pattern Recognition vol. 2, pp.402 – 405,Sept 2000. Digital Object Identifier 10.1109/ICPR.2000.906097
  6. Yong-zhi Li, Feng Ming, Jing-yu Yang and Ren-Liang Pan, “An Efficient Method of Nonlinear Feature Extraction Based on SVM,” Proc. 9th International Conference on Control, Automation, Robotics and Vision,ICARCV '06.pp.1 – 6,Dec 2006. Digital Object Identifier 10.1109/ICARCV.2006.345461
  7. Andreas Hanemann and Martin Sailer, “A Framework for Service Quality Assuranceusing Event Correlation Techniques” Proc. Σ Advanced Industrial Conference on Telecommunications/Service Assurance with Partial and Intermittent Resources Conference/ ELearning on Telecommunications Workshop, pp.428 – 433,July 2005. Digital Object Identifier 10.1109/AICT.2005.7
  8. U Oulu wood and knots database [Online]. Available: http://www.ee.oulu.fi/~olli/Projects/Lumber.Grading.html
Index Terms

Computer Science
Information Sciences

Keywords

Feature Extraction Correlation Coefficient Quality Checking Defects of Wood