Circular Hough Transform and Local Circularity Measure for Weight Estimation of a Graph-Cut based Wood Stack Measurement

Bo Galsgaard, Dennis Holm Lundtoft, Ivan Adriyanov Nikolov, Kamal Nasrollahi, Thomas B. Moeslund

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

6 Citations (Scopus)
493 Downloads (Pure)

Abstract

One of the time consuming tasks in the timber industry
is the manually measurement of features of wood stacks.
Such features include, but are not limited to, the number
of the logs in a stack, their diameters distribution, and their
volumes. Computer vision techniques have recently been
used for solving this real-world industrial application. Such
techniques are facing many challenges as the task is usually
performed in outdoor, uncontrolled, environments. Furthermore,
the logs can vary in texture and they can be occluded
by different obstacles. These all make the segmentation of
the wood logs a difficult task. Graph-cut has shown to be
good enough for such a segmentation. However, it is hard
to find proper graph weights. This is exactly the contribution
of this paper to propose a method for setting the
weights of the graph. To do so, we use Circular Hough
Transform (CHT) for obtaining information about the foreand
background regions of a stack image, and then use this
together with a Local Circularity Measure (LCM) to modify
the weights of the graph to segment the wood logs from the
rest of the image. We further improve the segmentation by
separating overlapping logs. These segmented wood logs
are finally scaled and used to acquire the necessary wood
stack measurements in real-world scale (in cm). The proposed
system, which works automatically, has been tested
on two different datasets, containing real outdoor images
of logs which vary in shapes and sizes. The experimental
results show that the proposed approach not only achieves
the same results as the state-of-the-art systems, it produces
more stable results.
Original languageEnglish
Title of host publicationIEEE Winter Conference on Applications of Computer Vision (WACV), 2015
PublisherIEEE Computer Society Press
Publication date6 Jan 2015
Pages686-693
ISBN (Print)978-1-4799-6683-7
DOIs
Publication statusPublished - 6 Jan 2015
EventIEEE Winter Conference on Applications of Computer Vision (WACV) - Waikoloa Beach, Hawaii, United States
Duration: 6 Jan 20158 Jan 2015

Conference

ConferenceIEEE Winter Conference on Applications of Computer Vision (WACV)
CountryUnited States
CityWaikoloa Beach, Hawaii
Period06/01/201508/01/2015

Fingerprint

Hough transforms
Wood
Timber
Computer vision
Industrial applications
Textures

Keywords

  • wood log segmentation
  • circular Hough Transform
  • Graph cuts

Cite this

Galsgaard, B., Lundtoft, D. H., Nikolov, I. A., Nasrollahi, K., & Moeslund, T. B. (2015). Circular Hough Transform and Local Circularity Measure for Weight Estimation of a Graph-Cut based Wood Stack Measurement. In IEEE Winter Conference on Applications of Computer Vision (WACV), 2015 (pp. 686-693). IEEE Computer Society Press. https://doi.org/10.1109/WACV.2015.97
Galsgaard, Bo ; Lundtoft, Dennis Holm ; Nikolov, Ivan Adriyanov ; Nasrollahi, Kamal ; Moeslund, Thomas B. / Circular Hough Transform and Local Circularity Measure for Weight Estimation of a Graph-Cut based Wood Stack Measurement. IEEE Winter Conference on Applications of Computer Vision (WACV), 2015. IEEE Computer Society Press, 2015. pp. 686-693
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title = "Circular Hough Transform and Local Circularity Measure for Weight Estimation of a Graph-Cut based Wood Stack Measurement",
abstract = "One of the time consuming tasks in the timber industryis the manually measurement of features of wood stacks.Such features include, but are not limited to, the numberof the logs in a stack, their diameters distribution, and theirvolumes. Computer vision techniques have recently beenused for solving this real-world industrial application. Suchtechniques are facing many challenges as the task is usuallyperformed in outdoor, uncontrolled, environments. Furthermore,the logs can vary in texture and they can be occludedby different obstacles. These all make the segmentation ofthe wood logs a difficult task. Graph-cut has shown to begood enough for such a segmentation. However, it is hardto find proper graph weights. This is exactly the contributionof this paper to propose a method for setting theweights of the graph. To do so, we use Circular HoughTransform (CHT) for obtaining information about the foreandbackground regions of a stack image, and then use thistogether with a Local Circularity Measure (LCM) to modifythe weights of the graph to segment the wood logs from therest of the image. We further improve the segmentation byseparating overlapping logs. These segmented wood logsare finally scaled and used to acquire the necessary woodstack measurements in real-world scale (in cm). The proposedsystem, which works automatically, has been testedon two different datasets, containing real outdoor imagesof logs which vary in shapes and sizes. The experimentalresults show that the proposed approach not only achievesthe same results as the state-of-the-art systems, it producesmore stable results.",
keywords = "wood log segmentation, circular Hough Transform, Graph cuts",
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Galsgaard, B, Lundtoft, DH, Nikolov, IA, Nasrollahi, K & Moeslund, TB 2015, Circular Hough Transform and Local Circularity Measure for Weight Estimation of a Graph-Cut based Wood Stack Measurement. in IEEE Winter Conference on Applications of Computer Vision (WACV), 2015. IEEE Computer Society Press, pp. 686-693, IEEE Winter Conference on Applications of Computer Vision (WACV), Waikoloa Beach, Hawaii, United States, 06/01/2015. https://doi.org/10.1109/WACV.2015.97

Circular Hough Transform and Local Circularity Measure for Weight Estimation of a Graph-Cut based Wood Stack Measurement. / Galsgaard, Bo; Lundtoft, Dennis Holm; Nikolov, Ivan Adriyanov; Nasrollahi, Kamal; Moeslund, Thomas B.

IEEE Winter Conference on Applications of Computer Vision (WACV), 2015. IEEE Computer Society Press, 2015. p. 686-693.

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

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AU - Galsgaard, Bo

AU - Lundtoft, Dennis Holm

AU - Nikolov, Ivan Adriyanov

AU - Nasrollahi, Kamal

AU - Moeslund, Thomas B.

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N2 - One of the time consuming tasks in the timber industryis the manually measurement of features of wood stacks.Such features include, but are not limited to, the numberof the logs in a stack, their diameters distribution, and theirvolumes. Computer vision techniques have recently beenused for solving this real-world industrial application. Suchtechniques are facing many challenges as the task is usuallyperformed in outdoor, uncontrolled, environments. Furthermore,the logs can vary in texture and they can be occludedby different obstacles. These all make the segmentation ofthe wood logs a difficult task. Graph-cut has shown to begood enough for such a segmentation. However, it is hardto find proper graph weights. This is exactly the contributionof this paper to propose a method for setting theweights of the graph. To do so, we use Circular HoughTransform (CHT) for obtaining information about the foreandbackground regions of a stack image, and then use thistogether with a Local Circularity Measure (LCM) to modifythe weights of the graph to segment the wood logs from therest of the image. We further improve the segmentation byseparating overlapping logs. These segmented wood logsare finally scaled and used to acquire the necessary woodstack measurements in real-world scale (in cm). The proposedsystem, which works automatically, has been testedon two different datasets, containing real outdoor imagesof logs which vary in shapes and sizes. The experimentalresults show that the proposed approach not only achievesthe same results as the state-of-the-art systems, it producesmore stable results.

AB - One of the time consuming tasks in the timber industryis the manually measurement of features of wood stacks.Such features include, but are not limited to, the numberof the logs in a stack, their diameters distribution, and theirvolumes. Computer vision techniques have recently beenused for solving this real-world industrial application. Suchtechniques are facing many challenges as the task is usuallyperformed in outdoor, uncontrolled, environments. Furthermore,the logs can vary in texture and they can be occludedby different obstacles. These all make the segmentation ofthe wood logs a difficult task. Graph-cut has shown to begood enough for such a segmentation. However, it is hardto find proper graph weights. This is exactly the contributionof this paper to propose a method for setting theweights of the graph. To do so, we use Circular HoughTransform (CHT) for obtaining information about the foreandbackground regions of a stack image, and then use thistogether with a Local Circularity Measure (LCM) to modifythe weights of the graph to segment the wood logs from therest of the image. We further improve the segmentation byseparating overlapping logs. These segmented wood logsare finally scaled and used to acquire the necessary woodstack measurements in real-world scale (in cm). The proposedsystem, which works automatically, has been testedon two different datasets, containing real outdoor imagesof logs which vary in shapes and sizes. The experimentalresults show that the proposed approach not only achievesthe same results as the state-of-the-art systems, it producesmore stable results.

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KW - circular Hough Transform

KW - Graph cuts

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DO - 10.1109/WACV.2015.97

M3 - Article in proceeding

SN - 978-1-4799-6683-7

SP - 686

EP - 693

BT - IEEE Winter Conference on Applications of Computer Vision (WACV), 2015

PB - IEEE Computer Society Press

ER -

Galsgaard B, Lundtoft DH, Nikolov IA, Nasrollahi K, Moeslund TB. Circular Hough Transform and Local Circularity Measure for Weight Estimation of a Graph-Cut based Wood Stack Measurement. In IEEE Winter Conference on Applications of Computer Vision (WACV), 2015. IEEE Computer Society Press. 2015. p. 686-693 https://doi.org/10.1109/WACV.2015.97