PithExtract: A robust algorithm for pith detection in computer tomography images of wood – Application to 125 logs from 17 tree species

Boukadida, Haykel; Longuetaud, Fleur; Colin, Frédéric; Freyburger, Charline; Constant, Thiéry; Leban, Jean-Michel; Mothe, Frédéric

doi:10.1016/j.compag.2012.03.012

Cited by 35 publications

(24 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A positioning system and a reference point (stem pith) were needed in order to successfully detect knots. First, the stem pith was recognized along the log length, for which a modification of the algorithm presented by Boukadida et al (2012) was used. In the sequence, the heartwood-sapwood, sapwoodbark, and outer border were recognized by applying a smoothing filter to remove the differences between earlywood and latewood.…”

Section: Automated Knot Detection Algorithm (Ct)mentioning

confidence: 99%

“…From there, they may present different trajectories and size distributions, in a growth process influenced by intricate spatiotemporal interactions, considering the tree vigor and its environment (Duchateau et al 2015). Due to variations in grain angle in and around knots (Longuetaud et al 2012), they represent a discontinuity in the uniform macroscopic wood matrix, generally leading to a loss in timber bending and tension strengths. As a consequence, knots can cause a considerable reduction in product volume (Fahey et al 1991) and value recovery from logs (Barbour and Parry 2001;Gartner 2005), particularly when considering industrial drying and grading in the production process.…”

Section: Introductionmentioning

confidence: 99%

“…Different solutions have been developed to use CT images in the most efficient way. Internal quality features that are detectable to varying degrees in CT images include: moisture content zones (Cherepanova and Hansson 2012;Osborne et al 2016), cracks (Andreu andRinnhofer 2003;Wehrhausen et al 2012), rot areas (Bhandarkar et al 1999), andknots (Andreu andRinnhofer 2003;Breinig et al 2012;Giudiceandrea et al 2011;Johansson et al 2013;Longuetaud et al 2012;Roussel et al 2014). Longuetaud et al (2012) also present a review of existing methods to automatically acquire the internal knot structure of a log based on CT images.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Validation of a CT knot detection algorithm on fresh Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) logs

Longo

Brüchert

Becker

et al. 2019

Annals of Forest Science

View full text Add to dashboard Cite

& Key message A fully automated algorithm allowed knot detection and positioning on computed tomography (CT) images of Douglas-fir logs. The detection of knot diameter and status could benefit from further improvements, i.e., testing other configurations and implementing texture measures. Manual measurement on CT images allows for tridimensional assessment and greater attainable sampling, while manual measurement on discs provides additional color and texture information. & Context Computed tomography (CT) is a very successful tool to non-destructively acquire the internal knot structure of a log. To enable large-scale applications, an algorithm that automatically detects knots is required. The accuracy of such algorithms depends heavily on the species and image resolution. & Aim This study validates a knot detection algorithm (Johansson et al. in Comput Electron Agric 96:238-45, 2013) on fresh Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) logs. & Methods In this study, 282 knots were sampled from 15 logs, selected from six 78-year-old trees in southwest Germany. The validation of the algorithm's knot detection was performed via comparison against two manual methods: on physical samples and on CT images. & Results The saturated sapwood negatively influences the overall knot detection, which causes underestimation of knot diameter in this area or incomplete detection. The algorithm tended to overestimate knot diameter, longitudinal position, and knot length. & Conclusion The algorithm provides the knot position with satisfactory accuracy. Other settings on contrast and considered volume around a knot can be tested within the algorithm, as well as new development and implementation of texture measures in the image analysis to improve the accuracy results for Douglas-fir in future investigations.

show abstract

Section: Automated Knot Detection Algorithm (Ct)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Validation of a CT knot detection algorithm on fresh Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) logs

Longo

Brüchert

Becker

et al. 2019

Annals of Forest Science

View full text Add to dashboard Cite

show abstract

“…This study provides a full framework for automatically delineating tree-rings in photographic images, which may solve major problems in dendrology, including, among other things, the detection of structures such as knots and piths (Boukadida et al, 2012). The point-based active contour method chosen, coupled with a DT-CWT-based gradient, proved its capacity to delineate tree-rings in images of Abies alba wood slices.…”

Section: Discussionmentioning

confidence: 99%

An automated method for tree-ring delineation based on active contours guided by DT-CWT complex coefficients in photographic images: Application to Abies alba wood slice images

Kennel

Borianne²,

Subsol

2015

Computers and Electronics in Agriculture

View full text Add to dashboard Cite

“…Many studies have proposed using the geometric relationships of the annual rings within the cross-section of wood stems to locate the pith (Kr€ ahenbühl et al, 2012;Boukadida et al, 2012;Kurdthongmee et al, 2018;Kurdthongmee and Suwannarat, 2019). Once the boundary of an annual ring has been extracted from an image, image processing routines are used to create segments from neighbouring pairs of pixels that form the boundary.…”

Section: Introductionmentioning

confidence: 99%

A comparative study of the effectiveness of using popular DNN object detection algorithms for pith detection in cross-sectional images of parawood

Kurdthongmee

2020

Heliyon

View full text Add to dashboard Cite

The location of pith in a cross-sectional surface of wood can be used to either evaluate its quality or guide the removal of soft wood from the wood stem. There have been many attempts to automate pith detection in images taken by a normal camera. The objective of this study is to comparatively study the effectiveness of two popular deep neural network (DNN) object detection algorithms for parawood pith detection in cross-sectional wood images. In the experiment, a database of 345 cross-sectional images of parawood, taken by a normal camera within a sawmill environment, was quadrupled in size via image augmentation. The images were then manually annotated to label the pith regions. The dataset was used to train two DNN object detection algorithms, an SSD (single shot detector) MobileNet and you-only-look-once (YOLO), via transfer learning. The inference results, utilizing pretrained models obtained by minimizing a loss function in both algorithms, were obtained on a separate dataset of 215 images and compared. The detection rate and average location error with respect to the ground truth were used to evaluate the effectiveness of detection. Additionally, the average distance error results were compared with the results of a state-of-the-art non-DNN algorithm. SSD MobileNet obtained the best detection rate of 87.7% with a ratio of training to test data of 80:20 and 152,000 training iterations. The average distance error of SSD MobileNet is comparable to that of YOLO and six times better than that of the non-DNN algorithm. Hence, SSD MobileNet is an effective approach to automating parawood pith detection in crosssectional images.

show abstract

PithExtract: A robust algorithm for pith detection in computer tomography images of wood – Application to 125 logs from 17 tree species

Cited by 35 publications

References 15 publications

Validation of a CT knot detection algorithm on fresh Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) logs

Validation of a CT knot detection algorithm on fresh Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) logs

An automated method for tree-ring delineation based on active contours guided by DT-CWT complex coefficients in photographic images: Application to Abies alba wood slice images

A comparative study of the effectiveness of using popular DNN object detection algorithms for pith detection in cross-sectional images of parawood

Contact Info

Product

Resources

About