@book{2018:reso:temporally,
title = {Temporally Consistent Superpixels},
year = {2018},
note = {This thesis addresses the field of early stage video
preprocessing in order to improve and accelerate subsequent processing
steps like semantic video segmentation or video-based object tracking.
A framework is proposed to segment video streams into temporally
consistent superpixels in order to create a representation of the
video with far less image primitives than the voxelgrid. The proposed
energy-minimization-based approach utilizes a novel hybrid clustering
strategy for a multidimensional feature space. Techniques are
presented to ensure the consistency of the superpixel flow with the
image movement while considering visual occlusion and
disocclusioneffects. The effectiveness of the proposed method is shown
by a comparison to state-of-theart spatio-temporal oversegmentation
algorithms using established benchmark metrics. Additionally, its
effectiveness is further demonstrated by showing its application on
the real-world scenario of interactive video
segmentation.KurzfassungEine ...},
edition = {1},
publisher = {VDI Verlag},
address = {Düsseldorf},
series = {Informatik/ Kommunikation},
volume = {861},
author = {Reso, Matthias}
}