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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Published in:	IET computer vision Vol. 9; no. 4; pp. 588 - 594
Main Authors:	Hirvonen, Juha, Kallio, Pasi
Format:	Journal Article
Language:	English
Published:	The Institution of Engineering and Technology 01.08.2015 Wiley
Subjects:	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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Abstract	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.
AbstractList	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. 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 [mu]m. Also, the choices made by the algorithm are much more consistent than the human choices.
Author	Kallio, Pasi Hirvonen, Juha
Author_xml	– sequence: 1 givenname: Juha surname: Hirvonen fullname: Hirvonen, Juha email: juha.hirvonen@tut.fi organization: Department of Automation Science and Engineering, Micro- and Nanosystems Research Group, Tampere University of Technology, Tampere, Finland – sequence: 2 givenname: Pasi surname: Kallio fullname: Kallio, Pasi organization: Department of Automation Science and Engineering, Micro- and Nanosystems Research Group, Tampere University of Technology, Tampere, Finland
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Keywords	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
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SubjectTerms	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
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Title	Automatic image-based detection and inspection of paper fibres for grasping
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