@article{1874,
  author = {ZHOU Jia-Nan, FENG Zhi-Lin, LIN Zi-Huai},
  title = {A Novel Defect Tracking Algorithm for Ink-jet Printing Video Based on Particle Filter Framework},
  journal = {Journal of Digital Information Management},
  year = {2015},
  volume = {13},
  number = {6},
  doi = {},
  url = {http://dline.info/fpaper/jdim/v13i6/v13i6_1.pdf},
  abstract = {Accurate and robust tracking of defect targets
in dynamic ink-jet printing videos is a huge challenge
in ink-jet printing technology for digital fabrication,
and becomes a popular topic for digital information applications
in ink-jet printing industry. Recently, particle filters
have drawn a significant amount of interest because
of their robust tracking performance. In this paper, a robust
defect tracking algorithm for ink-jet printing fabric
products was proposed in a particle filter framework. First,
the ink-jet printing defect tracking was regarded as a Bayesian
estimation problem, and a representative dynamic
state model was then defined prior to processing the video
data. Second, an observation model based on color histogram
was introduced to calculate the likelihood of sample
particles. Finally, a new motion constrained resampling
rule was designed by which the proposed algorithm can
track defect targets undergoing abrupt motion conditions
and background changes. Experimental results demonstrate
the effectiveness and superiority of the proposed
algorithm on tracking defect targets undergoing various
challenging conditions.},
}