2002
DOI: 10.1007/s00107-002-0287-z
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Rotfäuleerkennung bei Fichte in CT-Bildern

Abstract: Dieser Beitrag beschreibt die Erkennung von rotfäulebef-allenem Holz mittels Computertomographie und Holzfeuchtemessung. Durch die Erkennung innerer Holzfehler sollte es möglich sein, die Wertausbeute beim Einschnitt, durch Berücksichtigung der entstehenden Schnittholzqualitäten, zu erhöhen. Ein optisch erkennbar rotfaules Bloch wurde gescannt, wobei der Fäulebefall im rekonstruierten CT-Bild nicht festzustellen war. Die Abhängigkeit des Holzzustands (ob rotfäulebefallen oder nicht) von der CTNummer und der Ho… Show more

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Cited by 6 publications
(4 citation statements)
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“…However, Petutschnigg et al (2002) was not able to detect rot in Norway spruce logs using CT images, unless they added log moisture content in the regression analysis. The resulting identification accuracy was 80%.…”
Section: Other Defectsmentioning
confidence: 88%
See 1 more Smart Citation
“…However, Petutschnigg et al (2002) was not able to detect rot in Norway spruce logs using CT images, unless they added log moisture content in the regression analysis. The resulting identification accuracy was 80%.…”
Section: Other Defectsmentioning
confidence: 88%
“…Besides portable gamma-ray CT images (Habermehl and Ridder 1995;Niemz et al 1998), X-ray CT images have been tested for decay detection in logs (e.g., Benson-Cooper et al 1982;Petutschnigg et al 2002;Rinnhofer et al 2003;Rojas et al 2005Rojas et al , 2006 and in other wood products, such as historical wood specimens (Bahýl and Rohanová 2006). However, Petutschnigg et al (2002) was not able to detect rot in Norway spruce logs using CT images, unless they added log moisture content in the regression analysis.…”
Section: Other Defectsmentioning
confidence: 98%
“…Decay can be detected by CT scanning with high reliability, since decayed wood has a lower density and shows less attenuation of X-rays when compared to healthy wood (Sarigul et al 2000;Petutschnigg et al 2002). Even slight fluctuations in wood density can be detected, which allows the identification of decay areas that are not recognizable visually.…”
Section: Decay Detectionmentioning
confidence: 98%
“…A significant amount of research has been done using CT scanning of logs to map internal characteristics [292,293]. In addition to algorithms for knot detection [294][295][296], algorithms developed include pith detection [297], fiber orientation [298], spiral grain [299], decay recognition [300], and moisture distribution mapping [301]. Ultimately mapping internal log defects prior to sawing allows for improved lumber value recovery during processing [302][303][304].…”
Section: Computer Tomography (Ct) Scanningmentioning
confidence: 99%