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I. Janusch, W. Kropatsch:
"Novel concepts for recognition and representation of structure in spatio-temporal classes of images";
Vortrag: 20 th Computer Vision Winter Workshop, CVWW 2015, Österreich, Steiermark, Schloss Seggau; 09.02.2015 - 11.02.2015; in: "Proceedings of the 20th Computer Vision Winter Workshop Seggau, Austria", TU Graz, February 9 - 11, 2015 (2015), ISBN: 978-3-85125-388-7; S. 49 - 56.

This paper discusses open problems and
future research regarding the recognition and rep-
resentation of structures in sequences of either 2D
images or 3D data. All presented concepts aim at
improving the recognition of structure in data (espe-
cially by decreasing the influence of noise) and at
extending the representational power of known de-
scriptors (within the scope of this paper graphs and
skeletons). For the recognition of structure critical
points of a shape may be computed. We present an
approach to derive such critical points based on a
combination of skeletons and local features along a
skeleton. We further consider classes of data (for
example a temporal sequence of images of an ob-
ject), instead of a single data sample only. This so
called co-analysis reduces the sensitivity of analysis
to noise in the data. Moreover, a representative for a
whole class can be provided. Temporal sequences
may not only be used as a class of data in a co-
analysis process - focusing on the temporal aspect
and changes of the data over time an analysis of these
changes is needed. For this purpose we explore the
possibility to analyse a shape over time and to derive
a spatio-temporal representation. To extend the rep-
resentational power of skeletons we further present
an extension to skeletons using model fitting.

http://dx.doi.org/10.3217/978-3-85125-388-7

http://publik.tuwien.ac.at/files/PubDat_243223.pdf