Next Article in Journal
Connections between Weighted Generalized Cumulative Residual Entropy and Variance
Next Article in Special Issue
Multivariate Control Chart and Lee–Carter Models to Study Mortality Changes
Previous Article in Journal
Clustering-Based Binarization Methods Applied to the Crow Search Algorithm for 0/1 Combinatorial Problems
Previous Article in Special Issue
Simulating the Gluing of Wood Particles by Lattice Gas Cellular Automata and Random Walk
Open AccessArticle

Matching Score Models for Hyperspectral Range Analysis to Improve Wood Log Traceability by Fingerprint Methods

1
Department of Computer Sciences, University of Salzburg, 5020 Salzburg, Austria
2
Holztechnikum Kuchl, 5431 Kuchl, Austria
3
Department of Forest Products Technology and Timber Construction, University of Applied Sciences Salzburg, 5412 Puch bei Hallein, Austria
4
Center for Renewable Carbon, University of Tennessee, Knoxville, TN 37996, USA
*
Author to whom correspondence should be addressed.
Mathematics 2020, 8(7), 1071; https://doi.org/10.3390/math8071071
Received: 8 May 2020 / Revised: 9 June 2020 / Accepted: 14 June 2020 / Published: 2 July 2020
(This article belongs to the Special Issue Advances in Statistical Process Control and Their Applications)
Traceability of natural resources, from the cradle to the final product is a crucial issue to secure sustainable material usage as well as to optimize and control processes over the whole supply chain. In the forest products industries the material can be tracked by different technologies, but for the first step of material flow, from the forest to the industry, no systematic and complete technology has been developed. On the way to close this data gap the fingerprint technology for wooden logs looks promising. It uses inherent properties of a wood stem for identification. In this paper hyperspectral cameras are applied to gain images of Norway spruce (Picea abies [L.] Karst.) log end faces in different spectral ranges. The images are converted to a biometric template of feature vectors and a matching algorithm is used to evaluate if the biometric templates are similar or not. Based on this, matching scores specific spectral ranges which contain information to distinguish between different log end faces are identified. The method developed in this paper is a necessary and successful step to define scanning system parameters for fingerprint recognition systems for wood log traceability from the forest. View Full-Text
Keywords: wood traceability; biometric identification; fingerprint detection; hyperspectral imaging wood traceability; biometric identification; fingerprint detection; hyperspectral imaging
Show Figures

Figure 1

MDPI and ACS Style

Schraml, R.; Entacher, K.; Petutschnigg, A.; Young, T.; Uhl, A. Matching Score Models for Hyperspectral Range Analysis to Improve Wood Log Traceability by Fingerprint Methods. Mathematics 2020, 8, 1071. https://doi.org/10.3390/math8071071

AMA Style

Schraml R, Entacher K, Petutschnigg A, Young T, Uhl A. Matching Score Models for Hyperspectral Range Analysis to Improve Wood Log Traceability by Fingerprint Methods. Mathematics. 2020; 8(7):1071. https://doi.org/10.3390/math8071071

Chicago/Turabian Style

Schraml, Rudolf; Entacher, Karl; Petutschnigg, Alexander; Young, Timothy; Uhl, Andreas. 2020. "Matching Score Models for Hyperspectral Range Analysis to Improve Wood Log Traceability by Fingerprint Methods" Mathematics 8, no. 7: 1071. https://doi.org/10.3390/math8071071

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop