@INPROCEEDINGS{MMAR2016,
  author = {Okarma, K. and Fastowicz, J.},
  title = {No-Reference Quality Assessment of 3D Prints Based on the GLCM Analysis},
  booktitle = {Methods and Models in Automation and Robotics (MMAR), 2016 21st International
	Conference on},
  year = {2016},
  pages = {788-793},
  month = {Aug},
  publisher = {IEEE},
  abstract = {Quality assessment of 3D prints is one of the newest
challenges for machine vision. As the 3D printing is relatively
new technology, it is still far from perfection and there are
many static or dynamically changing factors which can affect
the quality of the final 3D prints. Considering the relatively long
time necessary for printing, it may be reasonable to interrupt
the printing process in order to save the filament (or another
material) and time in the case of decreased quality of already
printed fragment of an object. Such monitoring requires the use
of the video feedback with appropriate image analysis methods
which should allow a reliable quality assessment of the printed
object’s part. Typically such assessment should not be based
on the comparison with a reference image, as in many image
quality assessment methods, because such an image is usually
unavailable. Therefore in this paper an approach for the no-
reference quality assessment of the 3D prints based on the
analysis of the Gray-Level Co-occurrence Matrix (GLCM) and
chosen Haralick features is proposed and investigated. Obtained
experimental results demonstrate the validity and usefulness of
the proposed approach.}
}