Skip to page content
USDA Forest Service
  
Treesearch

Research & Development Treesearch

 
Treesearch Home
About Treesearch
Contact Us
Research & Development
Forest Products Lab
International Institute of Tropical Forestry
Northern
Pacific Northwest
Pacific Southwest
Rocky Mountain
Southern Research Station
Help
 

Science.gov - We Participate


USA.gov  Government Made Easy


Global Forest Information Service

US Forest Service
P.O. Box 96090
Washington, D.C.
20090-6090

(202) 205-8333

You are here: Home / Search / Publication Information
Bookmark and Share

Publication Information

View PDF (1.5 MB)

Title: Automated labeling of log features in CT imagery of multiple hardwood species

Author: Schmoldt, Daniel L.; He, Jing; Abbott, A. Lynn;

Date: 2000

Source: Wood and Fiber Science. 32(3): 287-300.

Publication Series: Miscellaneous Publication

Description: Before noninvasive scanning, e.g., computed tomography (CT), becomes feasible in industrial saw-mill operations, we need a procedure that can automatically interpret scan information in order to provide the saw operator with information necessary to make proper sawing decisions. To this end, we have worked to develop an approach for automatic analysis of CT images of hardwood logs. Our current approach classifies each pixel individually using a feed-forward artificial neural network (ANN) and feature vectors that include a small, local neighborhood of pixels and the distance of the target pixel to the center of the log. Initially, this ANN was able to classify clear wood, bark, decay, knots, and voids in CT images of two species of oak with 95% pixel-wise accuracy. Recently we have investigated other ANN classifiers, comparing 2-D versus 3-D neighborhoods and species-dependent (single species) versus species-independent (multiple species) classifiers using oak (Quercus rubra L. and Q. nigra L.), yellow-poplar (Liriodendron tulipifera L.), and black cherry (Prunus serotina Ehrh.) CT images. When considered individually, the resulting species-dependent classifiers yield similar levels of accuracy (96-98%). 3-D neighborhoods work better for multiple-species classifiers, and 2- D is better for the single-species case. Classifiers combining yellow-poplar and cherry data misclassify many pixels belonging to splits as clear wood, resulting in lower classification rates. If yellow-poplar was not paired with cherry, however, we found no statistical difference in accuracy between the single-and multiple-species classifiers.

Publication Notes:

  • We recommend that you also print this page and attach it to the printout of the article, to retain the full citation information.
  • This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.
  • You may send email to pubrequest@fs.fed.us to request a hard copy of this publication. (Please specify exactly which publication you are requesting and your mailing address.)

XML: View XML

Citation:


Schmoldt, Daniel L.; He, Jing; Abbott, A. Lynn 2000. Automated labeling of log features in CT imagery of multiple hardwood species. Wood and Fiber Science. 32(3): 287-300.

 


 [ Get Acrobat ]  Get the latest version of the Adobe Acrobat reader or Acrobat Reader for Windows with Search and Accessibility

USDA logo which links to the department's national site. Forest Service logo which links to the agency's national site.