Automatic image-based detection and inspection of paper fibres for grasping

An automatic computer vision algorithm that detects individual paper fibres from an image, assesses the possibility of grasping the detected fibres with microgrippers and detects the suitable grasping points is presented. The goal of the algorithm is to enable automatic fibre manipulation for mechan...

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Vydáno v:IET computer vision Ročník 9; číslo 4; s. 588 - 594
Hlavní autoři: Hirvonen, Juha, Kallio, Pasi
Médium: Journal Article
Jazyk:angličtina
Vydáno: The Institution of Engineering and Technology 01.08.2015
Wiley
Témata:
Algorithms
automatic computer vision algorithm
automatic fibre manipulation
automatic image-based detection
automatic image-based inspection
Automation
computational geometry
Computer vision
Fibers
Fibres
geometrical constraints
Grasping
grasping point detection
grippers
Human
image classification
Image detection
inspection
mechanical characterisation
microgrippers
micromanipulators
object detection
paper fibres
robot vision
automatic image-based detection
grippers
mechanical characterisation
image classification
fibres
paper fibres
computational geometry
robot vision
object detection
microgrippers
automatic computer vision algorithm
automatic fibre manipulation
paper
automatic image-based inspection
grasping point detection
geometrical constraints
micromanipulators
inspection
ISSN:1751-9632, 1751-9640, 1751-9640
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Shrnutí:An automatic computer vision algorithm that detects individual paper fibres from an image, assesses the possibility of grasping the detected fibres with microgrippers and detects the suitable grasping points is presented. The goal of the algorithm is to enable automatic fibre manipulation for mechanical characterisation, which has traditionally been slow manual work. The algorithm classifies the objects in images based on their morphology, and detects the proper grasp points from the individual fibres by applying given geometrical constraints. The authors test the ability of the algorithm to detect the individual fibres with 35 images containing more than 500 fibres in total, and also compare the graspability analysis and the calculated grasp points with the results of an experienced human operator with 15 images that contain a total of almost 200 fibres. The detection results are outstanding, with fewer than 1% of fibres missed. The graspability analysis gives sensitivity of 0.83 and specificity of 0.92, and the average distance between the grasp points of the human and the algorithm is 220 µm. Also, the choices made by the algorithm are much more consistent than the human choices.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1751-9632
1751-9640
1751-9640
DOI:10.1049/iet-cvi.2014.0416