A system for detection of internal log defects by computer analysis of axial CT images

Bhandarkar, Suchendra M.; Faust, Timothy D.; Tang, Mengjin

doi:10.1109/acv.1996.572064

Cited by 13 publications

(4 citation statements)

References 1 publication

(1 reference statement)

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“…In our opinion, Bhandarkar et al (1996;1999) gave the most finalised algorithm that we found in the literature. The first step consisted in the segmentation of CT images in four pixel classes (the knots belonged to the class with the highest density) by using a complex form of an area-based multiple thresholding algorithm.…”

Section: Review Of Existing Methods To Non-destructively and Automatimentioning

confidence: 85%

Automatic knot detection and measurements from X-ray CT images of wood: A review and validation of an improved algorithm on softwood samples

Longuetaud

Mothe

Kerautret

et al. 2012

Computers and Electronics in Agriculture

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An algorithm to automatically detect and measure knots in CT images of softwood beams was developed. The algorithm is based on the use of 3D connex components and a 3D distance transform constituting a newa p p r o a c h for knot diameter measurements. The present work was undertaken with the objective to automatically and non-destructively extract the distributions of knot characteristics within trees. These data are valuable for further studies related to tree development and tree architecture, and could even contribute to satisfying the current demand for automatic species identification on the basis of CT images. AreviewoftheliteratureaboutautomaticknotdetectioninX-ray CT images is provided. Relatively few references give quantitativelya c c u r a t er e s u l t so f knot measurements (i.e., not only knot localisation but knots i z ea n di n c l ination as well).

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Section: Review Of Existing Methods To Non-destructively and Automatimentioning

confidence: 85%

Automatic knot detection and measurements from X-ray CT images of wood: A review and validation of an improved algorithm on softwood samples

Longuetaud

Mothe

Kerautret

et al. 2012

Computers and Electronics in Agriculture

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“…In the literature, several methods have been proposed for pith detection on log cross-sections. Most of them [2,3,9,14,15,24] have been developed for X-ray computed tomographic (CT) images. The techniques based on CT images allow an efficient and robust detection of external and internal characteristics of tree logs, including the pith.…”

Section: Introductionmentioning

confidence: 99%

Pith Estimation on Tree Log End Images

Decelle¹,

Ngo²,

Debled-Rennesson³

et al. 2021

Reproducible Research in Pattern Recognition

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In this paper, we present an algorithm for pith estimation from digital images of wood cross-sections. The method is based on a probabilistic approach, namely ant colony optimization (ACO). After introducing the approach, we describe the implementation and the reproduction of the method linking to an online demonstration. Results show that the approach performs as well as state-of-the-art methods. The estimated pith is below 5mm from the ground truth. It is a fast method that could be used in real-time environment. This paper also gives the details about the intern parameter choice and shows how to use the C++ source code for testing, as well as provides limit cases of the proposed method and future improvements.

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“…Zhu et al [7] described a segmentation approach that relied heavily on edge and texture measures. Bhandarkar et al [8] described an correlationbased approach to defect detection. Schmoldt et al [9][10][11] described an approach that used artificial neural nets (ANN) to classify pixels individually, using small neighborhoods of CT density values as input feature vectors.…”

Section: Introductionmentioning

confidence: 99%

Nondestructive rule-based defect detection and identification system in CT images of hardwood logs

Sarigul¹,

Abbott²,

Schmoldt³

2001

AIP Conference Proceedings

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This paper is concerned with the detection of internal defects in hardwood logs. Because the commercial value of hardwood lumber is directly related to the quantity, type, and location of defects in the wood, sawing strategies are typically chosen in an attempt to minimize the defects in the resulting boards. Traditionally, the sawyer makes sawing decisions by visually examining the exterior of log and then revising the sawing strategy as more and more of the log's interior is exposed. Significantly better results would be expected if internal defects were known, so that a globally optimum solution could be selected in advance. This paper addresses this problem through the analysis of computed tomography (CT) images of logs, for the purpose of detecting important hardwood defects. In particular, the paper considers defect-dependent postprocessing methods, based on mathematical morphology, that show promising results.

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A system for detection of internal log defects by computer analysis of axial CT images

Cited by 13 publications

References 1 publication

Automatic knot detection and measurements from X-ray CT images of wood: A review and validation of an improved algorithm on softwood samples

Automatic knot detection and measurements from X-ray CT images of wood: A review and validation of an improved algorithm on softwood samples

Pith Estimation on Tree Log End Images

Nondestructive rule-based defect detection and identification system in CT images of hardwood logs

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