Characterization of fiber and vessel elements in pulp suspension images
Kurakina, Tatiana (2012)
Diplomityö
Kurakina, Tatiana
2012
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:fe201301221640
https://urn.fi/URN:NBN:fi:fe201301221640
Tiivistelmä
The thesis is related to the topic of image-based characterization of fibers in pulp suspension
during the papermaking process. Papermaking industry is focusing on process
control optimization and automatization, which makes it possible to manufacture highquality
products in a resource-efficient way. Being a part of the process control, pulp suspension
analysis allows to predict and modify properties of the end product. This work is
a part of the tree species identification task and focuses on analysis of fiber parameters in
the pulp suspension at the wet stage of paper production.
The existing machine vision methods for pulp characterization were investigated, and
a method exploiting direction sensitive filtering, non-maximum suppression, hysteresis
thresholding, tensor voting, and curve extraction from tensor maps was developed. Application
of the method to the microscopic grayscale pulp images made it possible to
detect curves corresponding to fibers in the pulp image and to compute their morphological
characteristics. Performance of the method was evaluated based on the manually
produced ground truth data. An accuracy of fiber characteristics estimation, including
length, width, and curvature, for the acacia pulp images was found to be 84, 85, and 60%
correspondingly.
during the papermaking process. Papermaking industry is focusing on process
control optimization and automatization, which makes it possible to manufacture highquality
products in a resource-efficient way. Being a part of the process control, pulp suspension
analysis allows to predict and modify properties of the end product. This work is
a part of the tree species identification task and focuses on analysis of fiber parameters in
the pulp suspension at the wet stage of paper production.
The existing machine vision methods for pulp characterization were investigated, and
a method exploiting direction sensitive filtering, non-maximum suppression, hysteresis
thresholding, tensor voting, and curve extraction from tensor maps was developed. Application
of the method to the microscopic grayscale pulp images made it possible to
detect curves corresponding to fibers in the pulp image and to compute their morphological
characteristics. Performance of the method was evaluated based on the manually
produced ground truth data. An accuracy of fiber characteristics estimation, including
length, width, and curvature, for the acacia pulp images was found to be 84, 85, and 60%
correspondingly.