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Computer Vision Measurements for Automated Microrobotic Paper Fiber Studies

Tutkimustuotos

Standard

Computer Vision Measurements for Automated Microrobotic Paper Fiber Studies. / Hirvonen, Juha.

Tampere University of Technology, 2017. 90 s. (Tampere University of Technology. Publication; Vuosikerta 1456).

Tutkimustuotos

Harvard

Hirvonen, J 2017, Computer Vision Measurements for Automated Microrobotic Paper Fiber Studies. Tampere University of Technology. Publication, Vuosikerta. 1456, Tampere University of Technology.

APA

Hirvonen, J. (2017). Computer Vision Measurements for Automated Microrobotic Paper Fiber Studies. (Tampere University of Technology. Publication; Vuosikerta 1456). Tampere University of Technology.

Vancouver

Hirvonen J. Computer Vision Measurements for Automated Microrobotic Paper Fiber Studies. Tampere University of Technology, 2017. 90 s. (Tampere University of Technology. Publication).

Author

Hirvonen, Juha. / Computer Vision Measurements for Automated Microrobotic Paper Fiber Studies. Tampere University of Technology, 2017. 90 Sivumäärä (Tampere University of Technology. Publication).

Bibtex - Lataa

@book{a49990256bc849609da51e950202b756,
title = "Computer Vision Measurements for Automated Microrobotic Paper Fiber Studies",
abstract = "The mechanical characterization of paper fibers and paper fiber bonds determines the key parameters affecting the mechanical properties of paper. Although bulk measurements from test sheets can give average values, they do not yield any real fiber-level data. The current, state-of-the-art methods for fiberlevel measurements are slow and laborious, requiring delicate manual handling of microscopic samples. There are commercial microrobotic actuators that allow automated or tele-operated manipulation of microscopic objects such as fibers, but it is challenging to acquire the data needed to guide such demanding manipulation. This thesis presents a solution to the illumination problem and computer vision algorithms for obtaining the required data. The solutions are designed for a microrobotic platform that comprises actuators for manipulating the fibers and one or two microscope cameras for visual feedback.The algorithms have been developed both for wet fibers, which can be treated as 2D objects, and for dry fibers and fiber bonds, which are treated as 3D objects. The major innovations in the algorithms are the rules for the micromanipulation of the curly fiber strands and the automated 3D measurements of microscale objects with random geometries. The solutions are validated by imaging and manipulation experiments with wet and dry paper fibers and dry paper fiber bonds. In the imaging experiments, the results are compared with the reference data obtained either from an experienced human or another imaging device. The results show that these solutions provide morphological data about the fibers which is accurate and precise enough to enable automated fiber manipulation. Although this thesis is focused on the manipulation of paper fibers and paper fiber bonds, both the illumination solution and the computer vision algorithms are applicable to other types of fibrous materials.",
author = "Juha Hirvonen",
year = "2017",
month = "2",
day = "10",
language = "English",
isbn = "978-952-15-3904-6",
series = "Tampere University of Technology. Publication",
publisher = "Tampere University of Technology",

}

RIS (suitable for import to EndNote) - Lataa

TY - BOOK

T1 - Computer Vision Measurements for Automated Microrobotic Paper Fiber Studies

AU - Hirvonen, Juha

PY - 2017/2/10

Y1 - 2017/2/10

N2 - The mechanical characterization of paper fibers and paper fiber bonds determines the key parameters affecting the mechanical properties of paper. Although bulk measurements from test sheets can give average values, they do not yield any real fiber-level data. The current, state-of-the-art methods for fiberlevel measurements are slow and laborious, requiring delicate manual handling of microscopic samples. There are commercial microrobotic actuators that allow automated or tele-operated manipulation of microscopic objects such as fibers, but it is challenging to acquire the data needed to guide such demanding manipulation. This thesis presents a solution to the illumination problem and computer vision algorithms for obtaining the required data. The solutions are designed for a microrobotic platform that comprises actuators for manipulating the fibers and one or two microscope cameras for visual feedback.The algorithms have been developed both for wet fibers, which can be treated as 2D objects, and for dry fibers and fiber bonds, which are treated as 3D objects. The major innovations in the algorithms are the rules for the micromanipulation of the curly fiber strands and the automated 3D measurements of microscale objects with random geometries. The solutions are validated by imaging and manipulation experiments with wet and dry paper fibers and dry paper fiber bonds. In the imaging experiments, the results are compared with the reference data obtained either from an experienced human or another imaging device. The results show that these solutions provide morphological data about the fibers which is accurate and precise enough to enable automated fiber manipulation. Although this thesis is focused on the manipulation of paper fibers and paper fiber bonds, both the illumination solution and the computer vision algorithms are applicable to other types of fibrous materials.

AB - The mechanical characterization of paper fibers and paper fiber bonds determines the key parameters affecting the mechanical properties of paper. Although bulk measurements from test sheets can give average values, they do not yield any real fiber-level data. The current, state-of-the-art methods for fiberlevel measurements are slow and laborious, requiring delicate manual handling of microscopic samples. There are commercial microrobotic actuators that allow automated or tele-operated manipulation of microscopic objects such as fibers, but it is challenging to acquire the data needed to guide such demanding manipulation. This thesis presents a solution to the illumination problem and computer vision algorithms for obtaining the required data. The solutions are designed for a microrobotic platform that comprises actuators for manipulating the fibers and one or two microscope cameras for visual feedback.The algorithms have been developed both for wet fibers, which can be treated as 2D objects, and for dry fibers and fiber bonds, which are treated as 3D objects. The major innovations in the algorithms are the rules for the micromanipulation of the curly fiber strands and the automated 3D measurements of microscale objects with random geometries. The solutions are validated by imaging and manipulation experiments with wet and dry paper fibers and dry paper fiber bonds. In the imaging experiments, the results are compared with the reference data obtained either from an experienced human or another imaging device. The results show that these solutions provide morphological data about the fibers which is accurate and precise enough to enable automated fiber manipulation. Although this thesis is focused on the manipulation of paper fibers and paper fiber bonds, both the illumination solution and the computer vision algorithms are applicable to other types of fibrous materials.

M3 - Doctoral thesis

SN - 978-952-15-3904-6

T3 - Tampere University of Technology. Publication

BT - Computer Vision Measurements for Automated Microrobotic Paper Fiber Studies

PB - Tampere University of Technology

ER -