Szirányi Tamás (SZTAKI)
Markov Random Field image segmentation and change detection
Change detection on images
of very different time instants from remote sensing databases and up-to-date
satellite born or UAV born imaging is an emerging technology platform today.
Since outdoor sceneries, principally observation of natural reserves,
agricultural meadows and forest areas, are changing in illumination, coloring,
textures and shadows time-by-time, and the resolution and geometrical
properties of the imaging conditions may be also diverse, robust and semantic
level algorithms should be developed for the comparison of images of the same
or similar places in very different times.A new
method, fusion Markov Random Field (fMRF) method has
been introduced which applied unsupervised or partly supervised clustering on a
fused image series by using cross-layer similarity measure, followed by a
multi-layer Markov Random Field segmentation. We show the MRF segmentation
method for the analysis of agricultural areas of fine details and difficult
subclasses.
The talk
is held in English!
Az előadás nyelve angol!
Date: Nov 15, Tuesday 4:15pm
Place: BME, Building „Q”,
Room QBF13