Skip to Main Content
Classifying features in CT imagery: accuracy for some single- and multiple-species classifiersAuthor(s): Daniel L. Schmoldt; Jing He; A. Lynn Abbott
Source: Proceedings, 3rd International Seminar/Workshop on Scanning Technology and Image Processing on Wood
Publication Series: Miscellaneous Publication
PDF: View PDF (172.93 KB)
DescriptionOur current approach to automatically label features in CT images of hardwood logs classifies each pixel of an image individually. These feature classifiers use a back-propagation 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 type of ANN was able to classify clear wood, bark, decay, knots, and voids in CT images of two species of oak (Quercus rubra, L., Quercus nigra, L.) 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, yellow poplar (Liriodendron tulipifera, L.), and black cherry (Prunus serotina, L.) CT images. When considered individually, the resulting speciesdependent classifiers yield similar levels of accuracy (96-98%); however, all classifiers achieve greater than 91% accuracy. 3-D neighborhoods work better for multiple-species classifiers and 2-D is better for single-species. Multiple-species classifiers, whose training included both cherry and yellow poplar examples, exhibit the lowest accuracy. Nevertheless, when this combination of species is avoided, there is no statistical difference in accuracy between single- and multiple-species classifiers, suggesting that a multiple-species classifier can be applied broadly with high accuracy. Because all reported accuracy values are prior to postprocessing operations (which visually improve classification accuracy), we are confident that even the least accurate classifiers would be adequate for industrial implementation.
- You may send email to firstname.lastname@example.org to request a hard copy of this publication.
- (Please specify exactly which publication you are requesting and your mailing address.)
- 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.
CitationSchmoldt, Daniel L.; He, Jing; Abbott, A. Lynn. 1998. Classifying features in CT imagery: accuracy for some single- and multiple-species classifiers. Proceedings, 3rd International Seminar/Workshop on Scanning Technology and Image Processing on Wood
- Automated labeling of log features in CT imagery of multiple hardwood species
- Rule-driven defect detection in CT images of hardwood logs
- Growth of Appalachian hardwoods kept free to grow from 2 to 12 years after clearcutting
XML: View XML